From 9fcd384918f66cdaebda288e94dde8dc04d2a5be Mon Sep 17 00:00:00 2001 From: Yev Meyer Date: Thu, 5 Feb 2026 17:53:43 -0500 Subject: [PATCH 1/2] simplify data parsing Signed-off-by: Yev Meyer --- resources_servers/text_to_sql/app.py | 27 ++++----------------------- 1 file changed, 4 insertions(+), 23 deletions(-) diff --git a/resources_servers/text_to_sql/app.py b/resources_servers/text_to_sql/app.py index c9e81a1c6..df3ebabe8 100644 --- a/resources_servers/text_to_sql/app.py +++ b/resources_servers/text_to_sql/app.py @@ -97,25 +97,6 @@ def extract_sql_from_response(text: str) -> Optional[str]: return None -def _extract_question_text(params: NeMoGymResponseCreateParamsNonStreaming) -> str: - """Extract the question text from the last user message.""" - last_text: Optional[str] = None - for m in params.input or []: - if getattr(m, "role", None) == "user": - c = getattr(m, "content", None) - if isinstance(c, str): - last_text = c - return (last_text or "").strip() - - -def _extract_dialect_from_prompt(text: str) -> Optional[str]: - """Extract SQL dialect from a structured prompt.""" - if not text: - return None - match = re.search(r"^\s*DIALECT:\s*([a-zA-Z0-9_+-]+)\s*$", text, re.MULTILINE) - return match.group(1) if match else None - - def _normalize_dialect(dialect: Optional[str]) -> Optional[str]: if not dialect: return None @@ -171,7 +152,7 @@ class TextToSqlRunRequest(BaseRunRequest): sql: str # Ground truth SQL query (required) sql_dialect: str # SQL dialect: mysql, postgresql, sqlite (required) sql_context: str = "" # Database schema (CREATE/INSERT statements) - sql_prompt: Optional[str] = None # Natural language question (optional, extracted from input if not provided) + sql_prompt: str # Natural language question (required) metadata: Optional[dict[str, Any]] = None @@ -196,7 +177,7 @@ class TextToSqlVerifyResponse(BaseVerifyResponse): extracted_sql: Optional[str] = None sql_dialect: str # SQL dialect used sql_context: str # Database schema provided - sql_prompt: Optional[str] = None # May be extracted from input + sql_prompt: str # Natural language question judge_passed: bool = False failure_reason: Optional[FailureCode] = None judge_evaluations: list[JudgeEvaluation] = [] @@ -235,8 +216,8 @@ async def verify(self, body: TextToSqlVerifyRequest) -> TextToSqlVerifyResponse: if sql_dialect not in SUPPORTED_DIALECTS: raise ValueError(f"Unsupported SQL dialect '{sql_dialect}'. Supported: {sorted(SUPPORTED_DIALECTS)}") - # Extract question from request field or from user message - sql_prompt = body.sql_prompt or _extract_question_text(body.responses_create_params) + # sql_prompt is a required field, validated by Pydantic + sql_prompt = body.sql_prompt # Get model output text directly from response generated = body.response.output_text or "" From 60f1c52ca5e215122addfdb892710b2dd9769e3f Mon Sep 17 00:00:00 2001 From: Yev Meyer Date: Thu, 5 Feb 2026 18:02:10 -0500 Subject: [PATCH 2/2] switch to xml tags Signed-off-by: Yev Meyer --- resources_servers/text_to_sql/README.md | 2 +- resources_servers/text_to_sql/data/example.jsonl | 10 +++++----- .../text_to_sql/data/example_rollouts.jsonl | 10 +++++----- 3 files changed, 11 insertions(+), 11 deletions(-) diff --git a/resources_servers/text_to_sql/README.md b/resources_servers/text_to_sql/README.md index 904256c18..b447bab06 100644 --- a/resources_servers/text_to_sql/README.md +++ b/resources_servers/text_to_sql/README.md @@ -41,7 +41,7 @@ Each data sample should include: }, { "role": "user", - "content": "DIALECT: postgresql\n\nDATABASE CONTEXT:\nCREATE TABLE users (id SERIAL PRIMARY KEY, name VARCHAR(100));\nINSERT INTO users VALUES (1, 'Alice'), (2, 'Bob');\n\nQUESTION:\nList all user names ordered alphabetically" + "content": "postgresql\n\n\nCREATE TABLE users (id SERIAL PRIMARY KEY, name VARCHAR(100));\nINSERT INTO users VALUES (1, 'Alice'), (2, 'Bob');\n\n\n\nList all user names ordered alphabetically\n" } ] }, diff --git a/resources_servers/text_to_sql/data/example.jsonl b/resources_servers/text_to_sql/data/example.jsonl index 3e684d46e..fb286165d 100644 --- a/resources_servers/text_to_sql/data/example.jsonl +++ b/resources_servers/text_to_sql/data/example.jsonl @@ -1,5 +1,5 @@ -{"responses_create_params": {"input": [{"role": "system", "content": "You are a SQL assistant who expertly maps natural language commands and problems into clean, efficient, and well-structured SQL queries. You always carefully examine the database context to include only the relevant tables and fields. You return only SQL code (no explanations outside of SQL code block; only proper comments are permitted). Wrap your SQL query inside a ```sql``` code block."}, {"role": "user", "content": "DIALECT: mysql\n\nDATABASE CONTEXT:\nCREATE TABLE weather_station (\n station_id INT UNSIGNED AUTO_INCREMENT PRIMARY KEY,\n station_name VARCHAR(100) NOT NULL,\n latitude DECIMAL(9,6) NOT NULL,\n longitude DECIMAL(9,6) NOT NULL,\n installed_date DATE NOT NULL,\n created_at DATETIME NOT NULL,\n updated_at DATETIME NOT NULL,\n is_active TINYINT(1) NOT NULL DEFAULT 1,\n description TEXT\n) ENGINE=InnoDB;\n\nCREATE TABLE sensor (\n sensor_id INT UNSIGNED AUTO_INCREMENT PRIMARY KEY,\n station_id INT UNSIGNED NOT NULL,\n sensor_type VARCHAR(50) NOT NULL,\n calibration_date DATE NOT NULL,\n serial_number VARCHAR(50),\n firmware_version VARCHAR(20),\n is_active TINYINT(1) NOT NULL DEFAULT 1,\n created_at DATETIME NOT NULL,\n updated_at DATETIME NOT NULL,\n description TEXT,\n FOREIGN KEY (station_id) REFERENCES weather_station(station_id)\n) ENGINE=InnoDB;\n\nCREATE TABLE sensor_log (\n log_id BIGINT UNSIGNED AUTO_INCREMENT PRIMARY KEY,\n sensor_id INT UNSIGNED NOT NULL,\n drift_date DATE NOT NULL,\n temperature_deviation DECIMAL(5,2) NOT NULL,\n reporting_email VARCHAR(150) NOT NULL,\n log_url VARCHAR(255) NOT NULL,\n readings TEXT NOT NULL,\n created_at DATETIME NOT NULL,\n updated_at DATETIME NOT NULL,\n is_processed TINYINT(1) NOT NULL DEFAULT 0,\n notes TEXT,\n FOREIGN KEY (sensor_id) REFERENCES sensor(sensor_id)\n) ENGINE=InnoDB;\n\nINSERT INTO weather_station (station_name, latitude, longitude, installed_date, created_at, updated_at, is_active, description) VALUES\n('North Ridge', 45.123456, -122.654321, '2023-03-15', '2023-04-01 08:00:00', '2023-04-01 08:00:00', 1, 'High elevation site'),\n('East Valley', 44.987654, -122.112233, '2022-11-20', '2022-12-01 09:15:00', '2022-12-01 09:15:00', 1, NULL);\n\nINSERT INTO sensor (station_id, sensor_type, calibration_date, serial_number, firmware_version, is_active, created_at, updated_at, description) VALUES\n(1, 'Thermometer', '2025-01-01', 'SN-001', 'v1.2.0', 1, '2025-01-02 08:00:00', '2025-01-02 08:00:00', NULL),\n(2, 'Thermometer', '2024-12-10', 'SN-003', 'v1.1.3', 1, '2024-12-12 09:00:00', '2024-12-12 09:00:00', NULL);\n\nINSERT INTO sensor_log (sensor_id, drift_date, temperature_deviation, reporting_email, log_url, readings, created_at, updated_at, is_processed, notes) VALUES\n(1, '2025-07-01', 0.45, 'tech.north@example.com', 'http://logs.example.com/north/2025-07-01', '[21.2,21.5,21.7]', '2025-07-02 08:10:00', '2025-07-02 08:10:00', 1, NULL),\n(1, '2025-07-15', 0.30, 'ops.north@example.com', 'http://logs.example.com/north/2025-07-15', '[21.0,21.1]', '2025-07-16 08:12:00', '2025-07-16 08:12:00', 0, NULL),\n(2, '2025-07-03', -0.20, 'maintenance@eastvalley.org', 'http://logs.example.com/east/2025-07-03', '[19.8,20.0,20.2,20.3]', '2025-07-04 09:00:00', '2025-07-04 09:00:00', 1, NULL);\n\nQUESTION:\nPlease retrieve for each weather station the latest sensor-drift date, the average temperature deviation, and the domain extracted from the reporting email within the sensor log URL, omitting any records whose embedded readings array contains fewer than three entries, and return the list ordered by descending average deviation."}]}, "sql": "WITH filtered_logs AS (\n SELECT\n sl.log_id,\n s.station_id,\n ws.station_name,\n sl.drift_date,\n sl.temperature_deviation,\n SUBSTRING_INDEX(sl.reporting_email, '@', -1) AS email_domain\n FROM sensor_log sl\n JOIN sensor s ON sl.sensor_id = s.sensor_id\n JOIN weather_station ws ON s.station_id = ws.station_id\n WHERE (LENGTH(sl.readings) - LENGTH(REPLACE(sl.readings, ',', ''))) >= 2\n),\nagg AS (\n SELECT\n station_id,\n station_name,\n MAX(drift_date) AS latest_drift_date,\n AVG(temperature_deviation) AS avg_temp_deviation\n FROM filtered_logs\n GROUP BY station_id, station_name\n),\nlatest_domain AS (\n SELECT station_id, email_domain\n FROM (\n SELECT\n fl.station_id,\n fl.email_domain,\n ROW_NUMBER() OVER (PARTITION BY fl.station_id ORDER BY fl.drift_date DESC) AS rn\n FROM filtered_logs fl\n ) t\n WHERE rn = 1\n)\nSELECT\n a.station_id,\n a.station_name,\n a.latest_drift_date,\n ROUND(a.avg_temp_deviation, 2) AS avg_temp_deviation,\n d.email_domain\nFROM agg a\nJOIN latest_domain d ON a.station_id = d.station_id\nORDER BY a.avg_temp_deviation DESC;", "sql_dialect": "mysql", "sql_prompt": "Please retrieve for each weather station the latest sensor-drift date, the average temperature deviation, and the domain extracted from the reporting email within the sensor log URL, omitting any records whose embedded readings array contains fewer than three entries, and return the list ordered by descending average deviation.", "sql_context": "CREATE TABLE weather_station (\n station_id INT UNSIGNED AUTO_INCREMENT PRIMARY KEY,\n station_name VARCHAR(100) NOT NULL,\n latitude DECIMAL(9,6) NOT NULL,\n longitude DECIMAL(9,6) NOT NULL,\n installed_date DATE NOT NULL,\n created_at DATETIME NOT NULL,\n updated_at DATETIME NOT NULL,\n is_active TINYINT(1) NOT NULL DEFAULT 1,\n description TEXT\n) ENGINE=InnoDB;\n\nCREATE TABLE sensor (\n sensor_id INT UNSIGNED AUTO_INCREMENT PRIMARY KEY,\n station_id INT UNSIGNED NOT NULL,\n sensor_type VARCHAR(50) NOT NULL,\n calibration_date DATE NOT NULL,\n serial_number VARCHAR(50),\n firmware_version VARCHAR(20),\n is_active TINYINT(1) NOT NULL DEFAULT 1,\n created_at DATETIME NOT NULL,\n updated_at DATETIME NOT NULL,\n description TEXT,\n FOREIGN KEY (station_id) REFERENCES weather_station(station_id)\n) ENGINE=InnoDB;\n\nCREATE TABLE sensor_log (\n log_id BIGINT UNSIGNED AUTO_INCREMENT PRIMARY KEY,\n sensor_id INT UNSIGNED NOT NULL,\n drift_date DATE NOT NULL,\n temperature_deviation DECIMAL(5,2) NOT NULL,\n reporting_email VARCHAR(150) NOT NULL,\n log_url VARCHAR(255) NOT NULL,\n readings TEXT NOT NULL,\n created_at DATETIME NOT NULL,\n updated_at DATETIME NOT NULL,\n is_processed TINYINT(1) NOT NULL DEFAULT 0,\n notes TEXT,\n FOREIGN KEY (sensor_id) REFERENCES sensor(sensor_id)\n) ENGINE=InnoDB;\n\nINSERT INTO weather_station (station_name, latitude, longitude, installed_date, created_at, updated_at, is_active, description) VALUES\n('North Ridge', 45.123456, -122.654321, '2023-03-15', '2023-04-01 08:00:00', '2023-04-01 08:00:00', 1, 'High elevation site'),\n('East Valley', 44.987654, -122.112233, '2022-11-20', '2022-12-01 09:15:00', '2022-12-01 09:15:00', 1, NULL);\n\nINSERT INTO sensor (station_id, sensor_type, calibration_date, serial_number, firmware_version, is_active, created_at, updated_at, description) VALUES\n(1, 'Thermometer', '2025-01-01', 'SN-001', 'v1.2.0', 1, '2025-01-02 08:00:00', '2025-01-02 08:00:00', NULL),\n(2, 'Thermometer', '2024-12-10', 'SN-003', 'v1.1.3', 1, '2024-12-12 09:00:00', '2024-12-12 09:00:00', NULL);\n\nINSERT INTO sensor_log (sensor_id, drift_date, temperature_deviation, reporting_email, log_url, readings, created_at, updated_at, is_processed, notes) VALUES\n(1, '2025-07-01', 0.45, 'tech.north@example.com', 'http://logs.example.com/north/2025-07-01', '[21.2,21.5,21.7]', '2025-07-02 08:10:00', '2025-07-02 08:10:00', 1, NULL),\n(1, '2025-07-15', 0.30, 'ops.north@example.com', 'http://logs.example.com/north/2025-07-15', '[21.0,21.1]', '2025-07-16 08:12:00', '2025-07-16 08:12:00', 0, NULL),\n(2, '2025-07-03', -0.20, 'maintenance@eastvalley.org', 'http://logs.example.com/east/2025-07-03', '[19.8,20.0,20.2,20.3]', '2025-07-04 09:00:00', '2025-07-04 09:00:00', 1, NULL);", "uuid": "weather-station-001"} -{"responses_create_params": {"input": [{"role": "system", "content": "You are a SQL assistant who expertly maps natural language commands and problems into clean, efficient, and well-structured SQL queries. You always carefully examine the database context to include only the relevant tables and fields. You return only SQL code (no explanations outside of SQL code block; only proper comments are permitted). Wrap your SQL query inside a ```sql``` code block."}, {"role": "user", "content": "DIALECT: postgresql\n\nDATABASE CONTEXT:\nCREATE TABLE customers (\n customer_id SERIAL PRIMARY KEY,\n name VARCHAR(100) NOT NULL,\n email VARCHAR(150) UNIQUE NOT NULL,\n created_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP\n);\n\nCREATE TABLE orders (\n order_id SERIAL PRIMARY KEY,\n customer_id INT REFERENCES customers(customer_id),\n order_date DATE NOT NULL,\n total_amount DECIMAL(10,2) NOT NULL,\n status VARCHAR(20) DEFAULT 'pending'\n);\n\nINSERT INTO customers (name, email) VALUES\n('Alice Smith', 'alice@example.com'),\n('Bob Jones', 'bob@example.com'),\n('Carol White', 'carol@example.com');\n\nINSERT INTO orders (customer_id, order_date, total_amount, status) VALUES\n(1, '2025-01-15', 150.00, 'completed'),\n(1, '2025-02-20', 200.00, 'completed'),\n(1, '2025-03-10', 75.00, 'completed'),\n(1, '2025-04-05', 125.00, 'completed'),\n(2, '2025-01-20', 300.00, 'completed'),\n(2, '2025-02-25', 450.00, 'pending');\n\nQUESTION:\nFind all customers who have placed more than 3 orders with a total spending exceeding $500, showing their name, email, order count, and total spent, sorted by total spent descending."}]}, "sql": "SELECT\n c.name,\n c.email,\n COUNT(o.order_id) AS order_count,\n SUM(o.total_amount) AS total_spent\nFROM customers c\nJOIN orders o ON c.customer_id = o.customer_id\nGROUP BY c.customer_id, c.name, c.email\nHAVING COUNT(o.order_id) > 3 AND SUM(o.total_amount) > 500\nORDER BY total_spent DESC;", "sql_dialect": "postgresql", "sql_prompt": "Find all customers who have placed more than 3 orders with a total spending exceeding $500, showing their name, email, order count, and total spent, sorted by total spent descending.", "sql_context": "CREATE TABLE customers (\n customer_id SERIAL PRIMARY KEY,\n name VARCHAR(100) NOT NULL,\n email VARCHAR(150) UNIQUE NOT NULL,\n created_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP\n);\n\nCREATE TABLE orders (\n order_id SERIAL PRIMARY KEY,\n customer_id INT REFERENCES customers(customer_id),\n order_date DATE NOT NULL,\n total_amount DECIMAL(10,2) NOT NULL,\n status VARCHAR(20) DEFAULT 'pending'\n);\n\nINSERT INTO customers (name, email) VALUES\n('Alice Smith', 'alice@example.com'),\n('Bob Jones', 'bob@example.com'),\n('Carol White', 'carol@example.com');\n\nINSERT INTO orders (customer_id, order_date, total_amount, status) VALUES\n(1, '2025-01-15', 150.00, 'completed'),\n(1, '2025-02-20', 200.00, 'completed'),\n(1, '2025-03-10', 75.00, 'completed'),\n(1, '2025-04-05', 125.00, 'completed'),\n(2, '2025-01-20', 300.00, 'completed'),\n(2, '2025-02-25', 450.00, 'pending');", "uuid": "ecommerce-001"} -{"responses_create_params": {"input": [{"role": "system", "content": "You are a SQL assistant who expertly maps natural language commands and problems into clean, efficient, and well-structured SQL queries. You always carefully examine the database context to include only the relevant tables and fields. You return only SQL code (no explanations outside of SQL code block; only proper comments are permitted). Wrap your SQL query inside a ```sql``` code block."}, {"role": "user", "content": "DIALECT: sqlite\n\nDATABASE CONTEXT:\nCREATE TABLE employees (\n employee_id INTEGER PRIMARY KEY AUTOINCREMENT,\n first_name TEXT NOT NULL,\n last_name TEXT NOT NULL,\n department TEXT NOT NULL,\n hire_date TEXT NOT NULL,\n salary REAL NOT NULL,\n manager_id INTEGER REFERENCES employees(employee_id)\n);\n\nCREATE TABLE projects (\n project_id INTEGER PRIMARY KEY AUTOINCREMENT,\n project_name TEXT NOT NULL,\n start_date TEXT NOT NULL,\n end_date TEXT,\n budget REAL NOT NULL\n);\n\nCREATE TABLE project_assignments (\n assignment_id INTEGER PRIMARY KEY AUTOINCREMENT,\n employee_id INTEGER REFERENCES employees(employee_id),\n project_id INTEGER REFERENCES projects(project_id),\n role TEXT NOT NULL,\n hours_allocated INTEGER NOT NULL\n);\n\nINSERT INTO employees (first_name, last_name, department, hire_date, salary) VALUES\n('John', 'Doe', 'Engineering', '2020-01-15', 85000),\n('Jane', 'Smith', 'Engineering', '2019-06-20', 92000),\n('Bob', 'Johnson', 'Sales', '2021-03-10', 75000);\n\nINSERT INTO projects (project_name, start_date, end_date, budget) VALUES\n('Project Alpha', '2025-01-01', '2025-06-30', 500000),\n('Project Beta', '2025-02-15', NULL, 750000),\n('Project Gamma', '2025-03-01', '2025-12-31', 300000);\n\nINSERT INTO project_assignments (employee_id, project_id, role, hours_allocated) VALUES\n(1, 1, 'Developer', 160),\n(1, 2, 'Lead Developer', 200),\n(2, 1, 'Architect', 120),\n(2, 2, 'Developer', 180),\n(2, 3, 'Consultant', 40);\n\nQUESTION:\nList all employees who are assigned to more than one project, showing their full name, department, and the number of projects they're working on."}]}, "sql": "SELECT\n e.first_name || ' ' || e.last_name AS full_name,\n e.department,\n COUNT(DISTINCT pa.project_id) AS project_count\nFROM employees e\nJOIN project_assignments pa ON e.employee_id = pa.employee_id\nGROUP BY e.employee_id, e.first_name, e.last_name, e.department\nHAVING COUNT(DISTINCT pa.project_id) > 1;", "sql_dialect": "sqlite", "sql_prompt": "List all employees who are assigned to more than one project, showing their full name, department, and the number of projects they're working on.", "sql_context": "CREATE TABLE employees (\n employee_id INTEGER PRIMARY KEY AUTOINCREMENT,\n first_name TEXT NOT NULL,\n last_name TEXT NOT NULL,\n department TEXT NOT NULL,\n hire_date TEXT NOT NULL,\n salary REAL NOT NULL,\n manager_id INTEGER REFERENCES employees(employee_id)\n);\n\nCREATE TABLE projects (\n project_id INTEGER PRIMARY KEY AUTOINCREMENT,\n project_name TEXT NOT NULL,\n start_date TEXT NOT NULL,\n end_date TEXT,\n budget REAL NOT NULL\n);\n\nCREATE TABLE project_assignments (\n assignment_id INTEGER PRIMARY KEY AUTOINCREMENT,\n employee_id INTEGER REFERENCES employees(employee_id),\n project_id INTEGER REFERENCES projects(project_id),\n role TEXT NOT NULL,\n hours_allocated INTEGER NOT NULL\n);\n\nINSERT INTO employees (first_name, last_name, department, hire_date, salary) VALUES\n('John', 'Doe', 'Engineering', '2020-01-15', 85000),\n('Jane', 'Smith', 'Engineering', '2019-06-20', 92000),\n('Bob', 'Johnson', 'Sales', '2021-03-10', 75000);\n\nINSERT INTO projects (project_name, start_date, end_date, budget) VALUES\n('Project Alpha', '2025-01-01', '2025-06-30', 500000),\n('Project Beta', '2025-02-15', NULL, 750000),\n('Project Gamma', '2025-03-01', '2025-12-31', 300000);\n\nINSERT INTO project_assignments (employee_id, project_id, role, hours_allocated) VALUES\n(1, 1, 'Developer', 160),\n(1, 2, 'Lead Developer', 200),\n(2, 1, 'Architect', 120),\n(2, 2, 'Developer', 180),\n(2, 3, 'Consultant', 40);", "uuid": "hr-projects-001"} -{"responses_create_params": {"input": [{"role": "system", "content": "You are a SQL assistant who expertly maps natural language commands and problems into clean, efficient, and well-structured SQL queries. You always carefully examine the database context to include only the relevant tables and fields. You return only SQL code (no explanations outside of SQL code block; only proper comments are permitted). Wrap your SQL query inside a ```sql``` code block."}, {"role": "user", "content": "DIALECT: mysql\n\nDATABASE CONTEXT:\nCREATE TABLE products (\n product_id INT UNSIGNED AUTO_INCREMENT PRIMARY KEY,\n product_name VARCHAR(200) NOT NULL,\n category VARCHAR(50) NOT NULL,\n price DECIMAL(10,2) NOT NULL,\n stock_quantity INT NOT NULL DEFAULT 0,\n created_at DATETIME DEFAULT CURRENT_TIMESTAMP\n) ENGINE=InnoDB;\n\nCREATE TABLE sales (\n sale_id INT UNSIGNED AUTO_INCREMENT PRIMARY KEY,\n product_id INT UNSIGNED NOT NULL,\n sale_date DATE NOT NULL,\n quantity_sold INT NOT NULL,\n revenue DECIMAL(10,2) NOT NULL,\n FOREIGN KEY (product_id) REFERENCES products(product_id)\n) ENGINE=InnoDB;\n\nINSERT INTO products (product_name, category, price, stock_quantity) VALUES\n('Laptop Pro', 'Electronics', 1299.99, 50),\n('Wireless Mouse', 'Electronics', 29.99, 200),\n('Office Chair', 'Furniture', 249.99, 75),\n('Standing Desk', 'Furniture', 599.99, 30),\n('USB Hub', 'Electronics', 39.99, 150);\n\nINSERT INTO sales (product_id, sale_date, quantity_sold, revenue) VALUES\n(1, '2025-01-15', 10, 12999.90),\n(2, '2025-01-15', 50, 1499.50),\n(1, '2025-02-10', 8, 10399.92),\n(3, '2025-02-15', 20, 4999.80),\n(4, '2025-02-20', 5, 2999.95),\n(2, '2025-03-01', 100, 2999.00),\n(5, '2025-03-05', 75, 2999.25);\n\nQUESTION:\nFor each product category, find the top-selling product (by total quantity sold) and show the category, product name, and total units sold."}]}, "sql": "WITH category_sales AS (\n SELECT\n p.category,\n p.product_id,\n p.product_name,\n SUM(s.quantity_sold) AS total_sold,\n ROW_NUMBER() OVER (PARTITION BY p.category ORDER BY SUM(s.quantity_sold) DESC) AS rn\n FROM products p\n JOIN sales s ON p.product_id = s.product_id\n GROUP BY p.category, p.product_id, p.product_name\n)\nSELECT category, product_name, total_sold\nFROM category_sales\nWHERE rn = 1;", "sql_dialect": "mysql", "sql_prompt": "For each product category, find the top-selling product (by total quantity sold) and show the category, product name, and total units sold.", "sql_context": "CREATE TABLE products (\n product_id INT UNSIGNED AUTO_INCREMENT PRIMARY KEY,\n product_name VARCHAR(200) NOT NULL,\n category VARCHAR(50) NOT NULL,\n price DECIMAL(10,2) NOT NULL,\n stock_quantity INT NOT NULL DEFAULT 0,\n created_at DATETIME DEFAULT CURRENT_TIMESTAMP\n) ENGINE=InnoDB;\n\nCREATE TABLE sales (\n sale_id INT UNSIGNED AUTO_INCREMENT PRIMARY KEY,\n product_id INT UNSIGNED NOT NULL,\n sale_date DATE NOT NULL,\n quantity_sold INT NOT NULL,\n revenue DECIMAL(10,2) NOT NULL,\n FOREIGN KEY (product_id) REFERENCES products(product_id)\n) ENGINE=InnoDB;\n\nINSERT INTO products (product_name, category, price, stock_quantity) VALUES\n('Laptop Pro', 'Electronics', 1299.99, 50),\n('Wireless Mouse', 'Electronics', 29.99, 200),\n('Office Chair', 'Furniture', 249.99, 75),\n('Standing Desk', 'Furniture', 599.99, 30),\n('USB Hub', 'Electronics', 39.99, 150);\n\nINSERT INTO sales (product_id, sale_date, quantity_sold, revenue) VALUES\n(1, '2025-01-15', 10, 12999.90),\n(2, '2025-01-15', 50, 1499.50),\n(1, '2025-02-10', 8, 10399.92),\n(3, '2025-02-15', 20, 4999.80),\n(4, '2025-02-20', 5, 2999.95),\n(2, '2025-03-01', 100, 2999.00),\n(5, '2025-03-05', 75, 2999.25);", "uuid": "sales-analytics-001"} -{"responses_create_params": {"input": [{"role": "system", "content": "You are a SQL assistant who expertly maps natural language commands and problems into clean, efficient, and well-structured SQL queries. You always carefully examine the database context to include only the relevant tables and fields. You return only SQL code (no explanations outside of SQL code block; only proper comments are permitted). Wrap your SQL query inside a ```sql``` code block."}, {"role": "user", "content": "DIALECT: postgresql\n\nDATABASE CONTEXT:\nCREATE TABLE users (\n user_id SERIAL PRIMARY KEY,\n username VARCHAR(50) UNIQUE NOT NULL,\n email VARCHAR(150) NOT NULL,\n registration_date DATE NOT NULL,\n is_premium BOOLEAN DEFAULT FALSE\n);\n\nCREATE TABLE posts (\n post_id SERIAL PRIMARY KEY,\n user_id INT REFERENCES users(user_id),\n title VARCHAR(200) NOT NULL,\n content TEXT,\n created_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP,\n view_count INT DEFAULT 0\n);\n\nCREATE TABLE comments (\n comment_id SERIAL PRIMARY KEY,\n post_id INT REFERENCES posts(post_id),\n user_id INT REFERENCES users(user_id),\n comment_text TEXT NOT NULL,\n created_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP\n);\n\nINSERT INTO users (username, email, registration_date, is_premium) VALUES\n('alice_dev', 'alice@example.com', '2024-01-15', TRUE),\n('bob_writer', 'bob@example.com', '2024-02-20', FALSE),\n('carol_blogger', 'carol@example.com', '2024-03-10', TRUE);\n\nINSERT INTO posts (user_id, title, content, view_count) VALUES\n(1, 'Getting Started with PostgreSQL', 'A beginner guide...', 1500),\n(1, 'Advanced SQL Techniques', 'Deep dive into...', 2300),\n(1, 'Database Optimization', 'Tips for performance...', 1800),\n(1, 'SQL Best Practices', 'Industry standards...', 900),\n(1, 'Query Tuning Guide', 'How to optimize...', 1200),\n(2, 'Writing Tips', 'How to write better...', 500),\n(3, 'My First Blog Post', 'Hello world!', 100);\n\nINSERT INTO comments (post_id, user_id, comment_text) VALUES\n(1, 2, 'Great introduction!'),\n(1, 3, 'Very helpful, thanks!'),\n(2, 2, 'Learned a lot from this'),\n(2, 3, 'Can you elaborate on CTEs?'),\n(3, 2, 'Nice tips!'),\n(4, 3, 'Following these now'),\n(5, 2, 'Bookmarked for later');\n\nQUESTION:\nFind the average number of comments per post for each user, but only include users who have created at least 5 posts. Show username, total posts, total comments received, and average comments per post."}]}, "sql": "SELECT\n u.username,\n COUNT(DISTINCT p.post_id) AS total_posts,\n COUNT(c.comment_id) AS total_comments,\n ROUND(COUNT(c.comment_id)::NUMERIC / COUNT(DISTINCT p.post_id), 2) AS avg_comments_per_post\nFROM users u\nJOIN posts p ON u.user_id = p.user_id\nLEFT JOIN comments c ON p.post_id = c.post_id\nGROUP BY u.user_id, u.username\nHAVING COUNT(DISTINCT p.post_id) >= 5\nORDER BY avg_comments_per_post DESC;", "sql_dialect": "postgresql", "sql_prompt": "Find the average number of comments per post for each user, but only include users who have created at least 5 posts. Show username, total posts, total comments received, and average comments per post.", "sql_context": "CREATE TABLE users (\n user_id SERIAL PRIMARY KEY,\n username VARCHAR(50) UNIQUE NOT NULL,\n email VARCHAR(150) NOT NULL,\n registration_date DATE NOT NULL,\n is_premium BOOLEAN DEFAULT FALSE\n);\n\nCREATE TABLE posts (\n post_id SERIAL PRIMARY KEY,\n user_id INT REFERENCES users(user_id),\n title VARCHAR(200) NOT NULL,\n content TEXT,\n created_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP,\n view_count INT DEFAULT 0\n);\n\nCREATE TABLE comments (\n comment_id SERIAL PRIMARY KEY,\n post_id INT REFERENCES posts(post_id),\n user_id INT REFERENCES users(user_id),\n comment_text TEXT NOT NULL,\n created_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP\n);\n\nINSERT INTO users (username, email, registration_date, is_premium) VALUES\n('alice_dev', 'alice@example.com', '2024-01-15', TRUE),\n('bob_writer', 'bob@example.com', '2024-02-20', FALSE),\n('carol_blogger', 'carol@example.com', '2024-03-10', TRUE);\n\nINSERT INTO posts (user_id, title, content, view_count) VALUES\n(1, 'Getting Started with PostgreSQL', 'A beginner guide...', 1500),\n(1, 'Advanced SQL Techniques', 'Deep dive into...', 2300),\n(1, 'Database Optimization', 'Tips for performance...', 1800),\n(1, 'SQL Best Practices', 'Industry standards...', 900),\n(1, 'Query Tuning Guide', 'How to optimize...', 1200),\n(2, 'Writing Tips', 'How to write better...', 500),\n(3, 'My First Blog Post', 'Hello world!', 100);\n\nINSERT INTO comments (post_id, user_id, comment_text) VALUES\n(1, 2, 'Great introduction!'),\n(1, 3, 'Very helpful, thanks!'),\n(2, 2, 'Learned a lot from this'),\n(2, 3, 'Can you elaborate on CTEs?'),\n(3, 2, 'Nice tips!'),\n(4, 3, 'Following these now'),\n(5, 2, 'Bookmarked for later');", "uuid": "social-media-001"} +{"responses_create_params": {"input": [{"role": "system", "content": "You are a SQL assistant who expertly maps natural language commands and problems into clean, efficient, and well-structured SQL queries. You always carefully examine the database context to include only the relevant tables and fields. You return only SQL code (no explanations outside of SQL code block; only proper comments are permitted). Wrap your SQL query inside a ```sql``` code block."}, {"role": "user", "content": "mysql\n\n\nCREATE TABLE weather_station (\n station_id INT UNSIGNED AUTO_INCREMENT PRIMARY KEY,\n station_name VARCHAR(100) NOT NULL,\n latitude DECIMAL(9,6) NOT NULL,\n longitude DECIMAL(9,6) NOT NULL,\n installed_date DATE NOT NULL,\n created_at DATETIME NOT NULL,\n updated_at DATETIME NOT NULL,\n is_active TINYINT(1) NOT NULL DEFAULT 1,\n description TEXT\n) ENGINE=InnoDB;\n\nCREATE TABLE sensor (\n sensor_id INT UNSIGNED AUTO_INCREMENT PRIMARY KEY,\n station_id INT UNSIGNED NOT NULL,\n sensor_type VARCHAR(50) NOT NULL,\n calibration_date DATE NOT NULL,\n serial_number VARCHAR(50),\n firmware_version VARCHAR(20),\n is_active TINYINT(1) NOT NULL DEFAULT 1,\n created_at DATETIME NOT NULL,\n updated_at DATETIME NOT NULL,\n description TEXT,\n FOREIGN KEY (station_id) REFERENCES weather_station(station_id)\n) ENGINE=InnoDB;\n\nCREATE TABLE sensor_log (\n log_id BIGINT UNSIGNED AUTO_INCREMENT PRIMARY KEY,\n sensor_id INT UNSIGNED NOT NULL,\n drift_date DATE NOT NULL,\n temperature_deviation DECIMAL(5,2) NOT NULL,\n reporting_email VARCHAR(150) NOT NULL,\n log_url VARCHAR(255) NOT NULL,\n readings TEXT NOT NULL,\n created_at DATETIME NOT NULL,\n updated_at DATETIME NOT NULL,\n is_processed TINYINT(1) NOT NULL DEFAULT 0,\n notes TEXT,\n FOREIGN KEY (sensor_id) REFERENCES sensor(sensor_id)\n) ENGINE=InnoDB;\n\nINSERT INTO weather_station (station_name, latitude, longitude, installed_date, created_at, updated_at, is_active, description) VALUES\n('North Ridge', 45.123456, -122.654321, '2023-03-15', '2023-04-01 08:00:00', '2023-04-01 08:00:00', 1, 'High elevation site'),\n('East Valley', 44.987654, -122.112233, '2022-11-20', '2022-12-01 09:15:00', '2022-12-01 09:15:00', 1, NULL);\n\nINSERT INTO sensor (station_id, sensor_type, calibration_date, serial_number, firmware_version, is_active, created_at, updated_at, description) VALUES\n(1, 'Thermometer', '2025-01-01', 'SN-001', 'v1.2.0', 1, '2025-01-02 08:00:00', '2025-01-02 08:00:00', NULL),\n(2, 'Thermometer', '2024-12-10', 'SN-003', 'v1.1.3', 1, '2024-12-12 09:00:00', '2024-12-12 09:00:00', NULL);\n\nINSERT INTO sensor_log (sensor_id, drift_date, temperature_deviation, reporting_email, log_url, readings, created_at, updated_at, is_processed, notes) VALUES\n(1, '2025-07-01', 0.45, 'tech.north@example.com', 'http://logs.example.com/north/2025-07-01', '[21.2,21.5,21.7]', '2025-07-02 08:10:00', '2025-07-02 08:10:00', 1, NULL),\n(1, '2025-07-15', 0.30, 'ops.north@example.com', 'http://logs.example.com/north/2025-07-15', '[21.0,21.1]', '2025-07-16 08:12:00', '2025-07-16 08:12:00', 0, NULL),\n(2, '2025-07-03', -0.20, 'maintenance@eastvalley.org', 'http://logs.example.com/east/2025-07-03', '[19.8,20.0,20.2,20.3]', '2025-07-04 09:00:00', '2025-07-04 09:00:00', 1, NULL);\n\n\n\nPlease retrieve for each weather station the latest sensor-drift date, the average temperature deviation, and the domain extracted from the reporting email within the sensor log URL, omitting any records whose embedded readings array contains fewer than three entries, and return the list ordered by descending average deviation."}]}, "sql": "WITH filtered_logs AS (\n SELECT\n sl.log_id,\n s.station_id,\n ws.station_name,\n sl.drift_date,\n sl.temperature_deviation,\n SUBSTRING_INDEX(sl.reporting_email, '@', -1) AS email_domain\n FROM sensor_log sl\n JOIN sensor s ON sl.sensor_id = s.sensor_id\n JOIN weather_station ws ON s.station_id = ws.station_id\n WHERE (LENGTH(sl.readings) - LENGTH(REPLACE(sl.readings, ',', ''))) >= 2\n),\nagg AS (\n SELECT\n station_id,\n station_name,\n MAX(drift_date) AS latest_drift_date,\n AVG(temperature_deviation) AS avg_temp_deviation\n FROM filtered_logs\n GROUP BY station_id, station_name\n),\nlatest_domain AS (\n SELECT station_id, email_domain\n FROM (\n SELECT\n fl.station_id,\n fl.email_domain,\n ROW_NUMBER() OVER (PARTITION BY fl.station_id ORDER BY fl.drift_date DESC) AS rn\n FROM filtered_logs fl\n ) t\n WHERE rn = 1\n)\nSELECT\n a.station_id,\n a.station_name,\n a.latest_drift_date,\n ROUND(a.avg_temp_deviation, 2) AS avg_temp_deviation,\n d.email_domain\nFROM agg a\nJOIN latest_domain d ON a.station_id = d.station_id\nORDER BY a.avg_temp_deviation DESC;", "sql_dialect": "mysql", "sql_prompt": "Please retrieve for each weather station the latest sensor-drift date, the average temperature deviation, and the domain extracted from the reporting email within the sensor log URL, omitting any records whose embedded readings array contains fewer than three entries, and return the list ordered by descending average deviation.", "sql_context": "CREATE TABLE weather_station (\n station_id INT UNSIGNED AUTO_INCREMENT PRIMARY KEY,\n station_name VARCHAR(100) NOT NULL,\n latitude DECIMAL(9,6) NOT NULL,\n longitude DECIMAL(9,6) NOT NULL,\n installed_date DATE NOT NULL,\n created_at DATETIME NOT NULL,\n updated_at DATETIME NOT NULL,\n is_active TINYINT(1) NOT NULL DEFAULT 1,\n description TEXT\n) ENGINE=InnoDB;\n\nCREATE TABLE sensor (\n sensor_id INT UNSIGNED AUTO_INCREMENT PRIMARY KEY,\n station_id INT UNSIGNED NOT NULL,\n sensor_type VARCHAR(50) NOT NULL,\n calibration_date DATE NOT NULL,\n serial_number VARCHAR(50),\n firmware_version VARCHAR(20),\n is_active TINYINT(1) NOT NULL DEFAULT 1,\n created_at DATETIME NOT NULL,\n updated_at DATETIME NOT NULL,\n description TEXT,\n FOREIGN KEY (station_id) REFERENCES weather_station(station_id)\n) ENGINE=InnoDB;\n\nCREATE TABLE sensor_log (\n log_id BIGINT UNSIGNED AUTO_INCREMENT PRIMARY KEY,\n sensor_id INT UNSIGNED NOT NULL,\n drift_date DATE NOT NULL,\n temperature_deviation DECIMAL(5,2) NOT NULL,\n reporting_email VARCHAR(150) NOT NULL,\n log_url VARCHAR(255) NOT NULL,\n readings TEXT NOT NULL,\n created_at DATETIME NOT NULL,\n updated_at DATETIME NOT NULL,\n is_processed TINYINT(1) NOT NULL DEFAULT 0,\n notes TEXT,\n FOREIGN KEY (sensor_id) REFERENCES sensor(sensor_id)\n) ENGINE=InnoDB;\n\nINSERT INTO weather_station (station_name, latitude, longitude, installed_date, created_at, updated_at, is_active, description) VALUES\n('North Ridge', 45.123456, -122.654321, '2023-03-15', '2023-04-01 08:00:00', '2023-04-01 08:00:00', 1, 'High elevation site'),\n('East Valley', 44.987654, -122.112233, '2022-11-20', '2022-12-01 09:15:00', '2022-12-01 09:15:00', 1, NULL);\n\nINSERT INTO sensor (station_id, sensor_type, calibration_date, serial_number, firmware_version, is_active, created_at, updated_at, description) VALUES\n(1, 'Thermometer', '2025-01-01', 'SN-001', 'v1.2.0', 1, '2025-01-02 08:00:00', '2025-01-02 08:00:00', NULL),\n(2, 'Thermometer', '2024-12-10', 'SN-003', 'v1.1.3', 1, '2024-12-12 09:00:00', '2024-12-12 09:00:00', NULL);\n\nINSERT INTO sensor_log (sensor_id, drift_date, temperature_deviation, reporting_email, log_url, readings, created_at, updated_at, is_processed, notes) VALUES\n(1, '2025-07-01', 0.45, 'tech.north@example.com', 'http://logs.example.com/north/2025-07-01', '[21.2,21.5,21.7]', '2025-07-02 08:10:00', '2025-07-02 08:10:00', 1, NULL),\n(1, '2025-07-15', 0.30, 'ops.north@example.com', 'http://logs.example.com/north/2025-07-15', '[21.0,21.1]', '2025-07-16 08:12:00', '2025-07-16 08:12:00', 0, NULL),\n(2, '2025-07-03', -0.20, 'maintenance@eastvalley.org', 'http://logs.example.com/east/2025-07-03', '[19.8,20.0,20.2,20.3]', '2025-07-04 09:00:00', '2025-07-04 09:00:00', 1, NULL);", "uuid": "weather-station-001"} +{"responses_create_params": {"input": [{"role": "system", "content": "You are a SQL assistant who expertly maps natural language commands and problems into clean, efficient, and well-structured SQL queries. You always carefully examine the database context to include only the relevant tables and fields. You return only SQL code (no explanations outside of SQL code block; only proper comments are permitted). Wrap your SQL query inside a ```sql``` code block."}, {"role": "user", "content": "postgresql\n\n\nCREATE TABLE customers (\n customer_id SERIAL PRIMARY KEY,\n name VARCHAR(100) NOT NULL,\n email VARCHAR(150) UNIQUE NOT NULL,\n created_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP\n);\n\nCREATE TABLE orders (\n order_id SERIAL PRIMARY KEY,\n customer_id INT REFERENCES customers(customer_id),\n order_date DATE NOT NULL,\n total_amount DECIMAL(10,2) NOT NULL,\n status VARCHAR(20) DEFAULT 'pending'\n);\n\nINSERT INTO customers (name, email) VALUES\n('Alice Smith', 'alice@example.com'),\n('Bob Jones', 'bob@example.com'),\n('Carol White', 'carol@example.com');\n\nINSERT INTO orders (customer_id, order_date, total_amount, status) VALUES\n(1, '2025-01-15', 150.00, 'completed'),\n(1, '2025-02-20', 200.00, 'completed'),\n(1, '2025-03-10', 75.00, 'completed'),\n(1, '2025-04-05', 125.00, 'completed'),\n(2, '2025-01-20', 300.00, 'completed'),\n(2, '2025-02-25', 450.00, 'pending');\n\n\n\nFind all customers who have placed more than 3 orders with a total spending exceeding $500, showing their name, email, order count, and total spent, sorted by total spent descending."}]}, "sql": "SELECT\n c.name,\n c.email,\n COUNT(o.order_id) AS order_count,\n SUM(o.total_amount) AS total_spent\nFROM customers c\nJOIN orders o ON c.customer_id = o.customer_id\nGROUP BY c.customer_id, c.name, c.email\nHAVING COUNT(o.order_id) > 3 AND SUM(o.total_amount) > 500\nORDER BY total_spent DESC;", "sql_dialect": "postgresql", "sql_prompt": "Find all customers who have placed more than 3 orders with a total spending exceeding $500, showing their name, email, order count, and total spent, sorted by total spent descending.", "sql_context": "CREATE TABLE customers (\n customer_id SERIAL PRIMARY KEY,\n name VARCHAR(100) NOT NULL,\n email VARCHAR(150) UNIQUE NOT NULL,\n created_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP\n);\n\nCREATE TABLE orders (\n order_id SERIAL PRIMARY KEY,\n customer_id INT REFERENCES customers(customer_id),\n order_date DATE NOT NULL,\n total_amount DECIMAL(10,2) NOT NULL,\n status VARCHAR(20) DEFAULT 'pending'\n);\n\nINSERT INTO customers (name, email) VALUES\n('Alice Smith', 'alice@example.com'),\n('Bob Jones', 'bob@example.com'),\n('Carol White', 'carol@example.com');\n\nINSERT INTO orders (customer_id, order_date, total_amount, status) VALUES\n(1, '2025-01-15', 150.00, 'completed'),\n(1, '2025-02-20', 200.00, 'completed'),\n(1, '2025-03-10', 75.00, 'completed'),\n(1, '2025-04-05', 125.00, 'completed'),\n(2, '2025-01-20', 300.00, 'completed'),\n(2, '2025-02-25', 450.00, 'pending');", "uuid": "ecommerce-001"} +{"responses_create_params": {"input": [{"role": "system", "content": "You are a SQL assistant who expertly maps natural language commands and problems into clean, efficient, and well-structured SQL queries. You always carefully examine the database context to include only the relevant tables and fields. You return only SQL code (no explanations outside of SQL code block; only proper comments are permitted). Wrap your SQL query inside a ```sql``` code block."}, {"role": "user", "content": "sqlite\n\n\nCREATE TABLE employees (\n employee_id INTEGER PRIMARY KEY AUTOINCREMENT,\n first_name TEXT NOT NULL,\n last_name TEXT NOT NULL,\n department TEXT NOT NULL,\n hire_date TEXT NOT NULL,\n salary REAL NOT NULL,\n manager_id INTEGER REFERENCES employees(employee_id)\n);\n\nCREATE TABLE projects (\n project_id INTEGER PRIMARY KEY AUTOINCREMENT,\n project_name TEXT NOT NULL,\n start_date TEXT NOT NULL,\n end_date TEXT,\n budget REAL NOT NULL\n);\n\nCREATE TABLE project_assignments (\n assignment_id INTEGER PRIMARY KEY AUTOINCREMENT,\n employee_id INTEGER REFERENCES employees(employee_id),\n project_id INTEGER REFERENCES projects(project_id),\n role TEXT NOT NULL,\n hours_allocated INTEGER NOT NULL\n);\n\nINSERT INTO employees (first_name, last_name, department, hire_date, salary) VALUES\n('John', 'Doe', 'Engineering', '2020-01-15', 85000),\n('Jane', 'Smith', 'Engineering', '2019-06-20', 92000),\n('Bob', 'Johnson', 'Sales', '2021-03-10', 75000);\n\nINSERT INTO projects (project_name, start_date, end_date, budget) VALUES\n('Project Alpha', '2025-01-01', '2025-06-30', 500000),\n('Project Beta', '2025-02-15', NULL, 750000),\n('Project Gamma', '2025-03-01', '2025-12-31', 300000);\n\nINSERT INTO project_assignments (employee_id, project_id, role, hours_allocated) VALUES\n(1, 1, 'Developer', 160),\n(1, 2, 'Lead Developer', 200),\n(2, 1, 'Architect', 120),\n(2, 2, 'Developer', 180),\n(2, 3, 'Consultant', 40);\n\n\n\nList all employees who are assigned to more than one project, showing their full name, department, and the number of projects they're working on."}]}, "sql": "SELECT\n e.first_name || ' ' || e.last_name AS full_name,\n e.department,\n COUNT(DISTINCT pa.project_id) AS project_count\nFROM employees e\nJOIN project_assignments pa ON e.employee_id = pa.employee_id\nGROUP BY e.employee_id, e.first_name, e.last_name, e.department\nHAVING COUNT(DISTINCT pa.project_id) > 1;", "sql_dialect": "sqlite", "sql_prompt": "List all employees who are assigned to more than one project, showing their full name, department, and the number of projects they're working on.", "sql_context": "CREATE TABLE employees (\n employee_id INTEGER PRIMARY KEY AUTOINCREMENT,\n first_name TEXT NOT NULL,\n last_name TEXT NOT NULL,\n department TEXT NOT NULL,\n hire_date TEXT NOT NULL,\n salary REAL NOT NULL,\n manager_id INTEGER REFERENCES employees(employee_id)\n);\n\nCREATE TABLE projects (\n project_id INTEGER PRIMARY KEY AUTOINCREMENT,\n project_name TEXT NOT NULL,\n start_date TEXT NOT NULL,\n end_date TEXT,\n budget REAL NOT NULL\n);\n\nCREATE TABLE project_assignments (\n assignment_id INTEGER PRIMARY KEY AUTOINCREMENT,\n employee_id INTEGER REFERENCES employees(employee_id),\n project_id INTEGER REFERENCES projects(project_id),\n role TEXT NOT NULL,\n hours_allocated INTEGER NOT NULL\n);\n\nINSERT INTO employees (first_name, last_name, department, hire_date, salary) VALUES\n('John', 'Doe', 'Engineering', '2020-01-15', 85000),\n('Jane', 'Smith', 'Engineering', '2019-06-20', 92000),\n('Bob', 'Johnson', 'Sales', '2021-03-10', 75000);\n\nINSERT INTO projects (project_name, start_date, end_date, budget) VALUES\n('Project Alpha', '2025-01-01', '2025-06-30', 500000),\n('Project Beta', '2025-02-15', NULL, 750000),\n('Project Gamma', '2025-03-01', '2025-12-31', 300000);\n\nINSERT INTO project_assignments (employee_id, project_id, role, hours_allocated) VALUES\n(1, 1, 'Developer', 160),\n(1, 2, 'Lead Developer', 200),\n(2, 1, 'Architect', 120),\n(2, 2, 'Developer', 180),\n(2, 3, 'Consultant', 40);", "uuid": "hr-projects-001"} +{"responses_create_params": {"input": [{"role": "system", "content": "You are a SQL assistant who expertly maps natural language commands and problems into clean, efficient, and well-structured SQL queries. You always carefully examine the database context to include only the relevant tables and fields. You return only SQL code (no explanations outside of SQL code block; only proper comments are permitted). Wrap your SQL query inside a ```sql``` code block."}, {"role": "user", "content": "mysql\n\n\nCREATE TABLE products (\n product_id INT UNSIGNED AUTO_INCREMENT PRIMARY KEY,\n product_name VARCHAR(200) NOT NULL,\n category VARCHAR(50) NOT NULL,\n price DECIMAL(10,2) NOT NULL,\n stock_quantity INT NOT NULL DEFAULT 0,\n created_at DATETIME DEFAULT CURRENT_TIMESTAMP\n) ENGINE=InnoDB;\n\nCREATE TABLE sales (\n sale_id INT UNSIGNED AUTO_INCREMENT PRIMARY KEY,\n product_id INT UNSIGNED NOT NULL,\n sale_date DATE NOT NULL,\n quantity_sold INT NOT NULL,\n revenue DECIMAL(10,2) NOT NULL,\n FOREIGN KEY (product_id) REFERENCES products(product_id)\n) ENGINE=InnoDB;\n\nINSERT INTO products (product_name, category, price, stock_quantity) VALUES\n('Laptop Pro', 'Electronics', 1299.99, 50),\n('Wireless Mouse', 'Electronics', 29.99, 200),\n('Office Chair', 'Furniture', 249.99, 75),\n('Standing Desk', 'Furniture', 599.99, 30),\n('USB Hub', 'Electronics', 39.99, 150);\n\nINSERT INTO sales (product_id, sale_date, quantity_sold, revenue) VALUES\n(1, '2025-01-15', 10, 12999.90),\n(2, '2025-01-15', 50, 1499.50),\n(1, '2025-02-10', 8, 10399.92),\n(3, '2025-02-15', 20, 4999.80),\n(4, '2025-02-20', 5, 2999.95),\n(2, '2025-03-01', 100, 2999.00),\n(5, '2025-03-05', 75, 2999.25);\n\n\n\nFor each product category, find the top-selling product (by total quantity sold) and show the category, product name, and total units sold."}]}, "sql": "WITH category_sales AS (\n SELECT\n p.category,\n p.product_id,\n p.product_name,\n SUM(s.quantity_sold) AS total_sold,\n ROW_NUMBER() OVER (PARTITION BY p.category ORDER BY SUM(s.quantity_sold) DESC) AS rn\n FROM products p\n JOIN sales s ON p.product_id = s.product_id\n GROUP BY p.category, p.product_id, p.product_name\n)\nSELECT category, product_name, total_sold\nFROM category_sales\nWHERE rn = 1;", "sql_dialect": "mysql", "sql_prompt": "For each product category, find the top-selling product (by total quantity sold) and show the category, product name, and total units sold.", "sql_context": "CREATE TABLE products (\n product_id INT UNSIGNED AUTO_INCREMENT PRIMARY KEY,\n product_name VARCHAR(200) NOT NULL,\n category VARCHAR(50) NOT NULL,\n price DECIMAL(10,2) NOT NULL,\n stock_quantity INT NOT NULL DEFAULT 0,\n created_at DATETIME DEFAULT CURRENT_TIMESTAMP\n) ENGINE=InnoDB;\n\nCREATE TABLE sales (\n sale_id INT UNSIGNED AUTO_INCREMENT PRIMARY KEY,\n product_id INT UNSIGNED NOT NULL,\n sale_date DATE NOT NULL,\n quantity_sold INT NOT NULL,\n revenue DECIMAL(10,2) NOT NULL,\n FOREIGN KEY (product_id) REFERENCES products(product_id)\n) ENGINE=InnoDB;\n\nINSERT INTO products (product_name, category, price, stock_quantity) VALUES\n('Laptop Pro', 'Electronics', 1299.99, 50),\n('Wireless Mouse', 'Electronics', 29.99, 200),\n('Office Chair', 'Furniture', 249.99, 75),\n('Standing Desk', 'Furniture', 599.99, 30),\n('USB Hub', 'Electronics', 39.99, 150);\n\nINSERT INTO sales (product_id, sale_date, quantity_sold, revenue) VALUES\n(1, '2025-01-15', 10, 12999.90),\n(2, '2025-01-15', 50, 1499.50),\n(1, '2025-02-10', 8, 10399.92),\n(3, '2025-02-15', 20, 4999.80),\n(4, '2025-02-20', 5, 2999.95),\n(2, '2025-03-01', 100, 2999.00),\n(5, '2025-03-05', 75, 2999.25);", "uuid": "sales-analytics-001"} +{"responses_create_params": {"input": [{"role": "system", "content": "You are a SQL assistant who expertly maps natural language commands and problems into clean, efficient, and well-structured SQL queries. You always carefully examine the database context to include only the relevant tables and fields. You return only SQL code (no explanations outside of SQL code block; only proper comments are permitted). Wrap your SQL query inside a ```sql``` code block."}, {"role": "user", "content": "postgresql\n\n\nCREATE TABLE users (\n user_id SERIAL PRIMARY KEY,\n username VARCHAR(50) UNIQUE NOT NULL,\n email VARCHAR(150) NOT NULL,\n registration_date DATE NOT NULL,\n is_premium BOOLEAN DEFAULT FALSE\n);\n\nCREATE TABLE posts (\n post_id SERIAL PRIMARY KEY,\n user_id INT REFERENCES users(user_id),\n title VARCHAR(200) NOT NULL,\n content TEXT,\n created_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP,\n view_count INT DEFAULT 0\n);\n\nCREATE TABLE comments (\n comment_id SERIAL PRIMARY KEY,\n post_id INT REFERENCES posts(post_id),\n user_id INT REFERENCES users(user_id),\n comment_text TEXT NOT NULL,\n created_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP\n);\n\nINSERT INTO users (username, email, registration_date, is_premium) VALUES\n('alice_dev', 'alice@example.com', '2024-01-15', TRUE),\n('bob_writer', 'bob@example.com', '2024-02-20', FALSE),\n('carol_blogger', 'carol@example.com', '2024-03-10', TRUE);\n\nINSERT INTO posts (user_id, title, content, view_count) VALUES\n(1, 'Getting Started with PostgreSQL', 'A beginner guide...', 1500),\n(1, 'Advanced SQL Techniques', 'Deep dive into...', 2300),\n(1, 'Database Optimization', 'Tips for performance...', 1800),\n(1, 'SQL Best Practices', 'Industry standards...', 900),\n(1, 'Query Tuning Guide', 'How to optimize...', 1200),\n(2, 'Writing Tips', 'How to write better...', 500),\n(3, 'My First Blog Post', 'Hello world!', 100);\n\nINSERT INTO comments (post_id, user_id, comment_text) VALUES\n(1, 2, 'Great introduction!'),\n(1, 3, 'Very helpful, thanks!'),\n(2, 2, 'Learned a lot from this'),\n(2, 3, 'Can you elaborate on CTEs?'),\n(3, 2, 'Nice tips!'),\n(4, 3, 'Following these now'),\n(5, 2, 'Bookmarked for later');\n\n\n\nFind the average number of comments per post for each user, but only include users who have created at least 5 posts. Show username, total posts, total comments received, and average comments per post."}]}, "sql": "SELECT\n u.username,\n COUNT(DISTINCT p.post_id) AS total_posts,\n COUNT(c.comment_id) AS total_comments,\n ROUND(COUNT(c.comment_id)::NUMERIC / COUNT(DISTINCT p.post_id), 2) AS avg_comments_per_post\nFROM users u\nJOIN posts p ON u.user_id = p.user_id\nLEFT JOIN comments c ON p.post_id = c.post_id\nGROUP BY u.user_id, u.username\nHAVING COUNT(DISTINCT p.post_id) >= 5\nORDER BY avg_comments_per_post DESC;", "sql_dialect": "postgresql", "sql_prompt": "Find the average number of comments per post for each user, but only include users who have created at least 5 posts. Show username, total posts, total comments received, and average comments per post.", "sql_context": "CREATE TABLE users (\n user_id SERIAL PRIMARY KEY,\n username VARCHAR(50) UNIQUE NOT NULL,\n email VARCHAR(150) NOT NULL,\n registration_date DATE NOT NULL,\n is_premium BOOLEAN DEFAULT FALSE\n);\n\nCREATE TABLE posts (\n post_id SERIAL PRIMARY KEY,\n user_id INT REFERENCES users(user_id),\n title VARCHAR(200) NOT NULL,\n content TEXT,\n created_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP,\n view_count INT DEFAULT 0\n);\n\nCREATE TABLE comments (\n comment_id SERIAL PRIMARY KEY,\n post_id INT REFERENCES posts(post_id),\n user_id INT REFERENCES users(user_id),\n comment_text TEXT NOT NULL,\n created_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP\n);\n\nINSERT INTO users (username, email, registration_date, is_premium) VALUES\n('alice_dev', 'alice@example.com', '2024-01-15', TRUE),\n('bob_writer', 'bob@example.com', '2024-02-20', FALSE),\n('carol_blogger', 'carol@example.com', '2024-03-10', TRUE);\n\nINSERT INTO posts (user_id, title, content, view_count) VALUES\n(1, 'Getting Started with PostgreSQL', 'A beginner guide...', 1500),\n(1, 'Advanced SQL Techniques', 'Deep dive into...', 2300),\n(1, 'Database Optimization', 'Tips for performance...', 1800),\n(1, 'SQL Best Practices', 'Industry standards...', 900),\n(1, 'Query Tuning Guide', 'How to optimize...', 1200),\n(2, 'Writing Tips', 'How to write better...', 500),\n(3, 'My First Blog Post', 'Hello world!', 100);\n\nINSERT INTO comments (post_id, user_id, comment_text) VALUES\n(1, 2, 'Great introduction!'),\n(1, 3, 'Very helpful, thanks!'),\n(2, 2, 'Learned a lot from this'),\n(2, 3, 'Can you elaborate on CTEs?'),\n(3, 2, 'Nice tips!'),\n(4, 3, 'Following these now'),\n(5, 2, 'Bookmarked for later');", "uuid": "social-media-001"} diff --git a/resources_servers/text_to_sql/data/example_rollouts.jsonl b/resources_servers/text_to_sql/data/example_rollouts.jsonl index 941a5414f..9aa949d82 100644 --- a/resources_servers/text_to_sql/data/example_rollouts.jsonl +++ b/resources_servers/text_to_sql/data/example_rollouts.jsonl @@ -1,5 +1,5 @@ -{"responses_create_params": {"background": null, "include": null, "input": [{"content": "You are a SQL assistant who expertly maps natural language commands and problems into clean, efficient, and well-structured SQL queries. You always carefully examine the database context to include only the relevant tables and fields. You return only SQL code (no explanations outside of SQL code block; only proper comments are permitted). Wrap your SQL query inside a ```sql``` code block.", "role": "system", "type": "message"}, {"content": "DIALECT: postgresql\n\nDATABASE CONTEXT:\nCREATE TABLE customers (\n customer_id SERIAL PRIMARY KEY,\n name VARCHAR(100) NOT NULL,\n email VARCHAR(150) UNIQUE NOT NULL,\n created_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP\n);\n\nCREATE TABLE orders (\n order_id SERIAL PRIMARY KEY,\n customer_id INT REFERENCES customers(customer_id),\n order_date DATE NOT NULL,\n total_amount DECIMAL(10,2) NOT NULL,\n status VARCHAR(20) DEFAULT 'pending'\n);\n\nINSERT INTO customers (name, email) VALUES\n('Alice Smith', 'alice@example.com'),\n('Bob Jones', 'bob@example.com'),\n('Carol White', 'carol@example.com');\n\nINSERT INTO orders (customer_id, order_date, total_amount, status) VALUES\n(1, '2025-01-15', 150.00, 'completed'),\n(1, '2025-02-20', 200.00, 'completed'),\n(1, '2025-03-10', 75.00, 'completed'),\n(1, '2025-04-05', 125.00, 'completed'),\n(2, '2025-01-20', 300.00, 'completed'),\n(2, '2025-02-25', 450.00, 'pending');\n\nQUESTION:\nFind all customers who have placed more than 3 orders with a total spending exceeding $500, showing their name, email, order count, and total spent, sorted by total spent descending.", "role": "user", "type": "message"}], "instructions": null, "max_output_tokens": null, "max_tool_calls": null, "metadata": null, "model": null, "parallel_tool_calls": true, "previous_response_id": null, "prompt": null, "reasoning": null, "service_tier": null, "store": null, "temperature": null, "text": null, "tool_choice": "auto", "tools": [], "top_logprobs": null, "top_p": null, "truncation": null, "user": null, "stream": null}, "response": {"id": "resp_04a358788f74523c006984e7fc7050819c9e4a9fd8c86d5beb", "created_at": 1770317820.0, "error": null, "incomplete_details": null, "instructions": null, "metadata": {}, "model": "gpt-4.1-2025-04-14", "object": "response", "output": [{"id": "msg_04a358788f74523c006984e7fcf164819c8e1845ddf2c39c07", "content": [{"annotations": [], "text": "```sql\nSELECT\n c.name,\n c.email,\n COUNT(o.order_id) AS order_count,\n SUM(o.total_amount) AS total_spent\nFROM\n customers c\nJOIN\n orders o ON c.customer_id = o.customer_id\nGROUP BY\n c.customer_id, c.name, c.email\nHAVING\n COUNT(o.order_id) > 3\n AND SUM(o.total_amount) > 500\nORDER BY\n total_spent DESC;\n```", "type": "output_text", "logprobs": []}], "role": "assistant", "status": "completed", "type": "message"}], "parallel_tool_calls": true, "temperature": 1.0, "tool_choice": "auto", "tools": [], "top_p": 1.0, "background": false, "conversation": null, "max_output_tokens": null, "max_tool_calls": null, "previous_response_id": null, "prompt": null, "prompt_cache_key": null, "reasoning": {"effort": null, "generate_summary": null, "summary": null}, "safety_identifier": null, "service_tier": "default", "status": "completed", "text": {"format": {"type": "text"}, "verbosity": "medium"}, "top_logprobs": 0, "truncation": "disabled", "usage": {"input_tokens": 395, "input_tokens_details": {"cached_tokens": 0}, "output_tokens": 103, "output_tokens_details": {"reasoning_tokens": 0}, "total_tokens": 498}, "user": null, "billing": {"payer": "developer"}, "completed_at": 1770317821, "frequency_penalty": 0.0, "presence_penalty": 0.0, "prompt_cache_retention": null, "store": true}, "reward": 1.0, "uuid": "ecommerce-001", "sql": "SELECT\n c.name,\n c.email,\n COUNT(o.order_id) AS order_count,\n SUM(o.total_amount) AS total_spent\nFROM customers c\nJOIN orders o ON c.customer_id = o.customer_id\nGROUP BY c.customer_id, c.name, c.email\nHAVING COUNT(o.order_id) > 3 AND SUM(o.total_amount) > 500\nORDER BY total_spent DESC;", "model_output": "```sql\nSELECT\n c.name,\n c.email,\n COUNT(o.order_id) AS order_count,\n SUM(o.total_amount) AS total_spent\nFROM\n customers c\nJOIN\n orders o ON c.customer_id = o.customer_id\nGROUP BY\n c.customer_id, c.name, c.email\nHAVING\n COUNT(o.order_id) > 3\n AND SUM(o.total_amount) > 500\nORDER BY\n total_spent DESC;\n```", "extracted_sql": "SELECT\n c.name,\n c.email,\n COUNT(o.order_id) AS order_count,\n SUM(o.total_amount) AS total_spent\nFROM\n customers c\nJOIN\n orders o ON c.customer_id = o.customer_id\nGROUP BY\n c.customer_id, c.name, c.email\nHAVING\n COUNT(o.order_id) > 3\n AND SUM(o.total_amount) > 500\nORDER BY\n total_spent DESC;", "sql_dialect": "postgresql", "sql_context": "CREATE TABLE customers (\n customer_id SERIAL PRIMARY KEY,\n name VARCHAR(100) NOT NULL,\n email VARCHAR(150) UNIQUE NOT NULL,\n created_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP\n);\n\nCREATE TABLE orders (\n order_id SERIAL PRIMARY KEY,\n customer_id INT REFERENCES customers(customer_id),\n order_date DATE NOT NULL,\n total_amount DECIMAL(10,2) NOT NULL,\n status VARCHAR(20) DEFAULT 'pending'\n);\n\nINSERT INTO customers (name, email) VALUES\n('Alice Smith', 'alice@example.com'),\n('Bob Jones', 'bob@example.com'),\n('Carol White', 'carol@example.com');\n\nINSERT INTO orders (customer_id, order_date, total_amount, status) VALUES\n(1, '2025-01-15', 150.00, 'completed'),\n(1, '2025-02-20', 200.00, 'completed'),\n(1, '2025-03-10', 75.00, 'completed'),\n(1, '2025-04-05', 125.00, 'completed'),\n(2, '2025-01-20', 300.00, 'completed'),\n(2, '2025-02-25', 450.00, 'pending');", "sql_prompt": "Find all customers who have placed more than 3 orders with a total spending exceeding $500, showing their name, email, order count, and total spent, sorted by total spent descending.", "judge_passed": true, "failure_reason": "none", "judge_evaluations": [{"responses_create_params": {"background": null, "include": null, "input": [{"content": "You are an expert SQL judge evaluating the functional equivalence of two SQL queries. You will be given SQL query A and SQL query B. Your task is to determine whether both queries would produce identical result sets when executed against the provided database schema.\n\n## Core Evaluation Principle\n\nTwo SQL queries are functionally equivalent if and only if they return the same rows with the same column values for all possible database states conforming to the schema. Focus on semantic equivalence, not syntactic similarity.\n\n## SQL Equivalence Rules\n\n### Always Equivalent (ignore these differences):\n- **Formatting**: Whitespace, line breaks, capitalization of keywords\n- **Aliasing**: Different table/column aliases (e.g., `e.name` vs `employees.name`)\n- **JOIN syntax**: `FROM a, b WHERE a.id = b.id` \u2261 `FROM a JOIN b ON a.id = b.id`\n- **JOIN keyword**: `JOIN` \u2261 `INNER JOIN`\n- **Explicit vs implicit column lists**: When results are identical\n- **Redundant DISTINCT**: When the query already guarantees uniqueness (e.g., selecting a PRIMARY KEY)\n- **Parentheses**: Extra parentheses that don't change evaluation order\n- **Semicolons**: Trailing semicolon presence/absence\n\n### Equivalent Patterns (require careful analysis):\n- **CTE vs Subquery**: `WITH t AS (SELECT ...) SELECT * FROM t` may equal a nested subquery\n- **EXISTS vs IN vs JOIN**: Often equivalent for the same filtering logic\n- **COALESCE vs CASE**: `COALESCE(a, b)` \u2261 `CASE WHEN a IS NOT NULL THEN a ELSE b END`\n- **Boolean expressions**: `NOT (a AND b)` \u2261 `(NOT a) OR (NOT b)` (De Morgan's laws)\n- **Arithmetic**: `price * 1.1` \u2261 `price + price * 0.1`\n- **Date arithmetic**: Dialect-specific but logically equivalent date calculations\n\n### Never Equivalent (these always matter):\n- **Different columns selected**: `SELECT a, b` \u2260 `SELECT a, c`\n- **Different filtering**: `WHERE x > 5` \u2260 `WHERE x >= 5`\n- **Different aggregations**: `SUM(x)` \u2260 `AVG(x)`\n- **Different grouping**: `GROUP BY a` \u2260 `GROUP BY a, b`\n- **Missing/extra DISTINCT**: When it changes the result set\n- **Different ORDER BY**: When ordering is semantically required by the question\n- **Different LIMIT/OFFSET**: When they affect the result set\n- **NULL handling differences**: When they produce different results\n\n## Dialect-Specific Considerations\n\n### MySQL\n- Backtick quoting for identifiers: `table_name`.`column_name`\n- LIMIT syntax: `LIMIT n` or `LIMIT offset, count`\n- String functions: `SUBSTRING_INDEX()`, `CONCAT()`, `GROUP_CONCAT()`\n- Auto-increment: `AUTO_INCREMENT`\n- Boolean: `TINYINT(1)`, `TRUE`/`FALSE`\n\n### PostgreSQL\n- Double-quote identifiers: \"column\"\n- Type casting: `value::type` or `CAST(value AS type)`\n- Array operations: `ANY()`, `ALL()`, array literals\n- String functions: `SPLIT_PART()`, `STRING_AGG()`\n- Sequences: `SERIAL`, `BIGSERIAL`\n- Boolean: Native `BOOLEAN` type\n\n### SQLite\n- Limited type system (TEXT, INTEGER, REAL, BLOB)\n- String concatenation: `||` operator\n- No native BOOLEAN (uses 0/1)\n- `AUTOINCREMENT` keyword\n- No RIGHT JOIN or FULL OUTER JOIN\n\n## Output Format\n\nAnalyze both queries step by step, then provide your verdict:\n- If the queries are functionally equivalent: [[A=B]]\n- If the queries produce different results: [[A!=B]]\n\nExample: \"After analyzing both queries, my verdict is [[A=B]]\".", "role": "system", "type": "message"}, {"content": "<|SQL Dialect|>\npostgresql\n\n<|Database Schema and Sample Data|>\nCREATE TABLE customers (\n customer_id SERIAL PRIMARY KEY,\n name VARCHAR(100) NOT NULL,\n email VARCHAR(150) UNIQUE NOT NULL,\n created_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP\n);\n\nCREATE TABLE orders (\n order_id SERIAL PRIMARY KEY,\n customer_id INT REFERENCES customers(customer_id),\n order_date DATE NOT NULL,\n total_amount DECIMAL(10,2) NOT NULL,\n status VARCHAR(20) DEFAULT 'pending'\n);\n\nINSERT INTO customers (name, email) VALUES\n('Alice Smith', 'alice@example.com'),\n('Bob Jones', 'bob@example.com'),\n('Carol White', 'carol@example.com');\n\nINSERT INTO orders (customer_id, order_date, total_amount, status) VALUES\n(1, '2025-01-15', 150.00, 'completed'),\n(1, '2025-02-20', 200.00, 'completed'),\n(1, '2025-03-10', 75.00, 'completed'),\n(1, '2025-04-05', 125.00, 'completed'),\n(2, '2025-01-20', 300.00, 'completed'),\n(2, '2025-02-25', 450.00, 'pending');\n\n<|Natural Language Question|>\nFind all customers who have placed more than 3 orders with a total spending exceeding $500, showing their name, email, order count, and total spent, sorted by total spent descending.\n\n<|Start of SQL Query A|>\nSELECT\n c.name,\n c.email,\n COUNT(o.order_id) AS order_count,\n SUM(o.total_amount) AS total_spent\nFROM customers c\nJOIN orders o ON c.customer_id = o.customer_id\nGROUP BY c.customer_id, c.name, c.email\nHAVING COUNT(o.order_id) > 3 AND SUM(o.total_amount) > 500\nORDER BY total_spent DESC;\n<|End of SQL Query A|>\n\n<|Start of SQL Query B|>\nSELECT\n c.name,\n c.email,\n COUNT(o.order_id) AS order_count,\n SUM(o.total_amount) AS total_spent\nFROM\n customers c\nJOIN\n orders o ON c.customer_id = o.customer_id\nGROUP BY\n c.customer_id, c.name, c.email\nHAVING\n COUNT(o.order_id) > 3\n AND SUM(o.total_amount) > 500\nORDER BY\n total_spent DESC;\n<|End of SQL Query B|>", "role": "user", "type": "message"}], "instructions": null, "max_output_tokens": null, "max_tool_calls": null, "metadata": null, "model": null, "parallel_tool_calls": true, "previous_response_id": null, "prompt": null, "reasoning": null, "service_tier": null, "store": null, "temperature": null, "text": null, "tool_choice": "auto", "tools": [], "top_logprobs": null, "top_p": null, "truncation": null, "user": null, "stream": null}, "response": {"id": "resp_0fc35cae913a11e3006984e7fdcf648197a67cc00a8f69163d", "created_at": 1770317821.0, "error": null, "incomplete_details": null, "instructions": null, "metadata": {}, "model": "gpt-4.1-2025-04-14", "object": "response", "output": [{"id": "msg_0fc35cae913a11e3006984e7fe37988197bcfccad874536d84", "content": [{"annotations": [], "text": "Let\u2019s analyze both queries step by step:\n\n### 1. Selected Columns\n\nBoth queries select:\n- `c.name`\n- `c.email`\n- `COUNT(o.order_id)` as `order_count`\n- `SUM(o.total_amount)` as `total_spent`\n\n### 2. FROM and JOIN Clauses\n\nBoth queries:\n- Start from `customers c`\n- Join to `orders o` on `c.customer_id = o.customer_id`\n\n### 3. GROUP BY Clause\n\nBoth queries group by:\n- `c.customer_id`\n- `c.name`\n- `c.email`\n\n(Given that `customer_id` is primary key, grouping by just `customer_id` is sufficient but including `name, email` is harmless.)\n\n### 4. HAVING Clause\n\nBoth have:\n- `HAVING COUNT(o.order_id) > 3 AND SUM(o.total_amount) > 500`\n\n### 5. ORDER BY Clause\n\nBoth have:\n- `ORDER BY total_spent DESC`\n\n### 6. Other Considerations\n\n- No use of DISTINCT, subqueries, or further filtering.\n- No syntactic or semantic differences\u2014except whitespace/formatting and keyword capitalization.\n\n#### Any possible differences?\n\nNo: Both are functionally, structurally, and semantically identical.\n\n---\n\n**After analyzing both queries, my verdict is [[A=B]].**", "type": "output_text", "logprobs": []}], "role": "assistant", "status": "completed", "type": "message"}], "parallel_tool_calls": true, "temperature": 1.0, "tool_choice": "auto", "tools": [], "top_p": 1.0, "background": false, "conversation": null, "max_output_tokens": null, "max_tool_calls": null, "previous_response_id": null, "prompt": null, "prompt_cache_key": null, "reasoning": {"effort": null, "generate_summary": null, "summary": null}, "safety_identifier": null, "service_tier": "default", "status": "completed", "text": {"format": {"type": "text"}, "verbosity": "medium"}, "top_logprobs": 0, "truncation": "disabled", "usage": {"input_tokens": 1408, "input_tokens_details": {"cached_tokens": 0}, "output_tokens": 278, "output_tokens_details": {"reasoning_tokens": 0}, "total_tokens": 1686}, "user": null, "billing": {"payer": "developer"}, "completed_at": 1770317826, "frequency_penalty": 0.0, "presence_penalty": 0.0, "prompt_cache_retention": null, "store": true}, "verdict_label": "[[A=B]]"}, {"responses_create_params": {"background": null, "include": null, "input": [{"content": "You are an expert SQL judge evaluating the functional equivalence of two SQL queries. You will be given SQL query A and SQL query B. Your task is to determine whether both queries would produce identical result sets when executed against the provided database schema.\n\n## Core Evaluation Principle\n\nTwo SQL queries are functionally equivalent if and only if they return the same rows with the same column values for all possible database states conforming to the schema. Focus on semantic equivalence, not syntactic similarity.\n\n## SQL Equivalence Rules\n\n### Always Equivalent (ignore these differences):\n- **Formatting**: Whitespace, line breaks, capitalization of keywords\n- **Aliasing**: Different table/column aliases (e.g., `e.name` vs `employees.name`)\n- **JOIN syntax**: `FROM a, b WHERE a.id = b.id` \u2261 `FROM a JOIN b ON a.id = b.id`\n- **JOIN keyword**: `JOIN` \u2261 `INNER JOIN`\n- **Explicit vs implicit column lists**: When results are identical\n- **Redundant DISTINCT**: When the query already guarantees uniqueness (e.g., selecting a PRIMARY KEY)\n- **Parentheses**: Extra parentheses that don't change evaluation order\n- **Semicolons**: Trailing semicolon presence/absence\n\n### Equivalent Patterns (require careful analysis):\n- **CTE vs Subquery**: `WITH t AS (SELECT ...) SELECT * FROM t` may equal a nested subquery\n- **EXISTS vs IN vs JOIN**: Often equivalent for the same filtering logic\n- **COALESCE vs CASE**: `COALESCE(a, b)` \u2261 `CASE WHEN a IS NOT NULL THEN a ELSE b END`\n- **Boolean expressions**: `NOT (a AND b)` \u2261 `(NOT a) OR (NOT b)` (De Morgan's laws)\n- **Arithmetic**: `price * 1.1` \u2261 `price + price * 0.1`\n- **Date arithmetic**: Dialect-specific but logically equivalent date calculations\n\n### Never Equivalent (these always matter):\n- **Different columns selected**: `SELECT a, b` \u2260 `SELECT a, c`\n- **Different filtering**: `WHERE x > 5` \u2260 `WHERE x >= 5`\n- **Different aggregations**: `SUM(x)` \u2260 `AVG(x)`\n- **Different grouping**: `GROUP BY a` \u2260 `GROUP BY a, b`\n- **Missing/extra DISTINCT**: When it changes the result set\n- **Different ORDER BY**: When ordering is semantically required by the question\n- **Different LIMIT/OFFSET**: When they affect the result set\n- **NULL handling differences**: When they produce different results\n\n## Dialect-Specific Considerations\n\n### MySQL\n- Backtick quoting for identifiers: `table_name`.`column_name`\n- LIMIT syntax: `LIMIT n` or `LIMIT offset, count`\n- String functions: `SUBSTRING_INDEX()`, `CONCAT()`, `GROUP_CONCAT()`\n- Auto-increment: `AUTO_INCREMENT`\n- Boolean: `TINYINT(1)`, `TRUE`/`FALSE`\n\n### PostgreSQL\n- Double-quote identifiers: \"column\"\n- Type casting: `value::type` or `CAST(value AS type)`\n- Array operations: `ANY()`, `ALL()`, array literals\n- String functions: `SPLIT_PART()`, `STRING_AGG()`\n- Sequences: `SERIAL`, `BIGSERIAL`\n- Boolean: Native `BOOLEAN` type\n\n### SQLite\n- Limited type system (TEXT, INTEGER, REAL, BLOB)\n- String concatenation: `||` operator\n- No native BOOLEAN (uses 0/1)\n- `AUTOINCREMENT` keyword\n- No RIGHT JOIN or FULL OUTER JOIN\n\n## Output Format\n\nAnalyze both queries step by step, then provide your verdict:\n- If the queries are functionally equivalent: [[A=B]]\n- If the queries produce different results: [[A!=B]]\n\nExample: \"After analyzing both queries, my verdict is [[A=B]]\".", "role": "system", "type": "message"}, {"content": "<|SQL Dialect|>\npostgresql\n\n<|Database Schema and Sample Data|>\nCREATE TABLE customers (\n customer_id SERIAL PRIMARY KEY,\n name VARCHAR(100) NOT NULL,\n email VARCHAR(150) UNIQUE NOT NULL,\n created_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP\n);\n\nCREATE TABLE orders (\n order_id SERIAL PRIMARY KEY,\n customer_id INT REFERENCES customers(customer_id),\n order_date DATE NOT NULL,\n total_amount DECIMAL(10,2) NOT NULL,\n status VARCHAR(20) DEFAULT 'pending'\n);\n\nINSERT INTO customers (name, email) VALUES\n('Alice Smith', 'alice@example.com'),\n('Bob Jones', 'bob@example.com'),\n('Carol White', 'carol@example.com');\n\nINSERT INTO orders (customer_id, order_date, total_amount, status) VALUES\n(1, '2025-01-15', 150.00, 'completed'),\n(1, '2025-02-20', 200.00, 'completed'),\n(1, '2025-03-10', 75.00, 'completed'),\n(1, '2025-04-05', 125.00, 'completed'),\n(2, '2025-01-20', 300.00, 'completed'),\n(2, '2025-02-25', 450.00, 'pending');\n\n<|Natural Language Question|>\nFind all customers who have placed more than 3 orders with a total spending exceeding $500, showing their name, email, order count, and total spent, sorted by total spent descending.\n\n<|Start of SQL Query A|>\nSELECT\n c.name,\n c.email,\n COUNT(o.order_id) AS order_count,\n SUM(o.total_amount) AS total_spent\nFROM\n customers c\nJOIN\n orders o ON c.customer_id = o.customer_id\nGROUP BY\n c.customer_id, c.name, c.email\nHAVING\n COUNT(o.order_id) > 3\n AND SUM(o.total_amount) > 500\nORDER BY\n total_spent DESC;\n<|End of SQL Query A|>\n\n<|Start of SQL Query B|>\nSELECT\n c.name,\n c.email,\n COUNT(o.order_id) AS order_count,\n SUM(o.total_amount) AS total_spent\nFROM customers c\nJOIN orders o ON c.customer_id = o.customer_id\nGROUP BY c.customer_id, c.name, c.email\nHAVING COUNT(o.order_id) > 3 AND SUM(o.total_amount) > 500\nORDER BY total_spent DESC;\n<|End of SQL Query B|>", "role": "user", "type": "message"}], "instructions": null, "max_output_tokens": null, "max_tool_calls": null, "metadata": null, "model": null, "parallel_tool_calls": true, "previous_response_id": null, "prompt": null, "reasoning": null, "service_tier": null, "store": null, "temperature": null, "text": null, "tool_choice": "auto", "tools": [], "top_logprobs": null, "top_p": null, "truncation": null, "user": null, "stream": null}, "response": {"id": "resp_011a0cb4b7c805ed006984e8030a548190b690058ff296c8d4", "created_at": 1770317827.0, "error": null, "incomplete_details": null, "instructions": null, "metadata": {}, "model": "gpt-4.1-2025-04-14", "object": "response", "output": [{"id": "msg_011a0cb4b7c805ed006984e8036e308190b73d562267efd0c5", "content": [{"annotations": [], "text": "Let's analyze both queries step by step.\n\n## Query A\n\n```sql\nSELECT\n c.name,\n c.email,\n COUNT(o.order_id) AS order_count,\n SUM(o.total_amount) AS total_spent\nFROM\n customers c\nJOIN\n orders o ON c.customer_id = o.customer_id\nGROUP BY\n c.customer_id, c.name, c.email\nHAVING\n COUNT(o.order_id) > 3\n AND SUM(o.total_amount) > 500\nORDER BY\n total_spent DESC;\n```\n\n## Query B\n\n```sql\nSELECT\n c.name,\n c.email,\n COUNT(o.order_id) AS order_count,\n SUM(o.total_amount) AS total_spent\nFROM customers c\nJOIN orders o ON c.customer_id = o.customer_id\nGROUP BY c.customer_id, c.name, c.email\nHAVING COUNT(o.order_id) > 3 AND SUM(o.total_amount) > 500\nORDER BY total_spent DESC;\n```\n\n## Analysis\n\nNow, let's decompose both queries on a clause-by-clause basis:\n\n### SELECT clause\n\nBoth queries select the same fields:\n- `c.name`\n- `c.email`\n- count of `o.order_id` as `order_count`\n- sum of `o.total_amount` as `total_spent`\n\n### FROM/JOIN clause\n\nBoth:\n- Refer to `customers c`\n- INNER JOIN with `orders o` on `c.customer_id = o.customer_id`\n\n### GROUP BY clause\n\nBoth:\n- Group by `c.customer_id, c.name, c.email`\n\n### HAVING clause\n\nBoth:\n- `COUNT(o.order_id) > 3`\n- `SUM(o.total_amount) > 500`\n\n### ORDER BY\n\nBoth:\n- `total_spent DESC`\n\n### Syntax/Formatting\n\nAll differences are superficial\u2014spacing and line breaks only.\n\n### Schema\n\nNo fields or aggregates are ambiguous or context-sensitive based on the schema supplied.\n\n## Verdict\n\nAfter analyzing both queries, my verdict is [[A=B]].", "type": "output_text", "logprobs": []}], "role": "assistant", "status": "completed", "type": "message"}], "parallel_tool_calls": true, "temperature": 1.0, "tool_choice": "auto", "tools": [], "top_p": 1.0, "background": false, "conversation": null, "max_output_tokens": null, "max_tool_calls": null, "previous_response_id": null, "prompt": null, "prompt_cache_key": null, "reasoning": {"effort": null, "generate_summary": null, "summary": null}, "safety_identifier": null, "service_tier": "default", "status": "completed", "text": {"format": {"type": "text"}, "verbosity": "medium"}, "top_logprobs": 0, "truncation": "disabled", "usage": {"input_tokens": 1408, "input_tokens_details": {"cached_tokens": 1024}, "output_tokens": 422, "output_tokens_details": {"reasoning_tokens": 0}, "total_tokens": 1830}, "user": null, "billing": {"payer": "developer"}, "completed_at": 1770317833, "frequency_penalty": 0.0, "presence_penalty": 0.0, "prompt_cache_retention": null, "store": true}, "verdict_label": "[[A=B]]"}], "metadata": null} -{"responses_create_params": {"background": null, "include": null, "input": [{"content": "You are a SQL assistant who expertly maps natural language commands and problems into clean, efficient, and well-structured SQL queries. You always carefully examine the database context to include only the relevant tables and fields. You return only SQL code (no explanations outside of SQL code block; only proper comments are permitted). Wrap your SQL query inside a ```sql``` code block.", "role": "system", "type": "message"}, {"content": "DIALECT: sqlite\n\nDATABASE CONTEXT:\nCREATE TABLE employees (\n employee_id INTEGER PRIMARY KEY AUTOINCREMENT,\n first_name TEXT NOT NULL,\n last_name TEXT NOT NULL,\n department TEXT NOT NULL,\n hire_date TEXT NOT NULL,\n salary REAL NOT NULL,\n manager_id INTEGER REFERENCES employees(employee_id)\n);\n\nCREATE TABLE projects (\n project_id INTEGER PRIMARY KEY AUTOINCREMENT,\n project_name TEXT NOT NULL,\n start_date TEXT NOT NULL,\n end_date TEXT,\n budget REAL NOT NULL\n);\n\nCREATE TABLE project_assignments (\n assignment_id INTEGER PRIMARY KEY AUTOINCREMENT,\n employee_id INTEGER REFERENCES employees(employee_id),\n project_id INTEGER REFERENCES projects(project_id),\n role TEXT NOT NULL,\n hours_allocated INTEGER NOT NULL\n);\n\nINSERT INTO employees (first_name, last_name, department, hire_date, salary) VALUES\n('John', 'Doe', 'Engineering', '2020-01-15', 85000),\n('Jane', 'Smith', 'Engineering', '2019-06-20', 92000),\n('Bob', 'Johnson', 'Sales', '2021-03-10', 75000);\n\nINSERT INTO projects (project_name, start_date, end_date, budget) VALUES\n('Project Alpha', '2025-01-01', '2025-06-30', 500000),\n('Project Beta', '2025-02-15', NULL, 750000),\n('Project Gamma', '2025-03-01', '2025-12-31', 300000);\n\nINSERT INTO project_assignments (employee_id, project_id, role, hours_allocated) VALUES\n(1, 1, 'Developer', 160),\n(1, 2, 'Lead Developer', 200),\n(2, 1, 'Architect', 120),\n(2, 2, 'Developer', 180),\n(2, 3, 'Consultant', 40);\n\nQUESTION:\nList all employees who are assigned to more than one project, showing their full name, department, and the number of projects they're working on.", "role": "user", "type": "message"}], "instructions": null, "max_output_tokens": null, "max_tool_calls": null, "metadata": null, "model": null, "parallel_tool_calls": true, "previous_response_id": null, "prompt": null, "reasoning": null, "service_tier": null, "store": null, "temperature": null, "text": null, "tool_choice": "auto", "tools": [], "top_logprobs": null, "top_p": null, "truncation": null, "user": null, "stream": null}, "response": {"id": "resp_03ce49329a4d3b43006984e7fc7a148197ab4b6f66478713b3", "created_at": 1770317820.0, "error": null, "incomplete_details": null, "instructions": null, "metadata": {}, "model": "gpt-4.1-2025-04-14", "object": "response", "output": [{"id": "msg_03ce49329a4d3b43006984e7fd14c08197a7d99557406f5af2", "content": [{"annotations": [], "text": "```sql\nSELECT \n e.first_name || ' ' || e.last_name AS full_name,\n e.department,\n COUNT(DISTINCT pa.project_id) AS projects_count\nFROM \n employees e\nJOIN \n project_assignments pa ON e.employee_id = pa.employee_id\nGROUP BY \n e.employee_id, e.first_name, e.last_name, e.department\nHAVING \n COUNT(DISTINCT pa.project_id) > 1;\n```", "type": "output_text", "logprobs": []}], "role": "assistant", "status": "completed", "type": "message"}], "parallel_tool_calls": true, "temperature": 1.0, "tool_choice": "auto", "tools": [], "top_p": 1.0, "background": false, "conversation": null, "max_output_tokens": null, "max_tool_calls": null, "previous_response_id": null, "prompt": null, "prompt_cache_key": null, "reasoning": {"effort": null, "generate_summary": null, "summary": null}, "safety_identifier": null, "service_tier": "default", "status": "completed", "text": {"format": {"type": "text"}, "verbosity": "medium"}, "top_logprobs": 0, "truncation": "disabled", "usage": {"input_tokens": 517, "input_tokens_details": {"cached_tokens": 0}, "output_tokens": 96, "output_tokens_details": {"reasoning_tokens": 0}, "total_tokens": 613}, "user": null, "billing": {"payer": "developer"}, "completed_at": 1770317821, "frequency_penalty": 0.0, "presence_penalty": 0.0, "prompt_cache_retention": null, "store": true}, "reward": 1.0, "uuid": "hr-projects-001", "sql": "SELECT\n e.first_name || ' ' || e.last_name AS full_name,\n e.department,\n COUNT(DISTINCT pa.project_id) AS project_count\nFROM employees e\nJOIN project_assignments pa ON e.employee_id = pa.employee_id\nGROUP BY e.employee_id, e.first_name, e.last_name, e.department\nHAVING COUNT(DISTINCT pa.project_id) > 1;", "model_output": "```sql\nSELECT \n e.first_name || ' ' || e.last_name AS full_name,\n e.department,\n COUNT(DISTINCT pa.project_id) AS projects_count\nFROM \n employees e\nJOIN \n project_assignments pa ON e.employee_id = pa.employee_id\nGROUP BY \n e.employee_id, e.first_name, e.last_name, e.department\nHAVING \n COUNT(DISTINCT pa.project_id) > 1;\n```", "extracted_sql": "SELECT \n e.first_name || ' ' || e.last_name AS full_name,\n e.department,\n COUNT(DISTINCT pa.project_id) AS projects_count\nFROM \n employees e\nJOIN \n project_assignments pa ON e.employee_id = pa.employee_id\nGROUP BY \n e.employee_id, e.first_name, e.last_name, e.department\nHAVING \n COUNT(DISTINCT pa.project_id) > 1;", "sql_dialect": "sqlite", "sql_context": "CREATE TABLE employees (\n employee_id INTEGER PRIMARY KEY AUTOINCREMENT,\n first_name TEXT NOT NULL,\n last_name TEXT NOT NULL,\n department TEXT NOT NULL,\n hire_date TEXT NOT NULL,\n salary REAL NOT NULL,\n manager_id INTEGER REFERENCES employees(employee_id)\n);\n\nCREATE TABLE projects (\n project_id INTEGER PRIMARY KEY AUTOINCREMENT,\n project_name TEXT NOT NULL,\n start_date TEXT NOT NULL,\n end_date TEXT,\n budget REAL NOT NULL\n);\n\nCREATE TABLE project_assignments (\n assignment_id INTEGER PRIMARY KEY AUTOINCREMENT,\n employee_id INTEGER REFERENCES employees(employee_id),\n project_id INTEGER REFERENCES projects(project_id),\n role TEXT NOT NULL,\n hours_allocated INTEGER NOT NULL\n);\n\nINSERT INTO employees (first_name, last_name, department, hire_date, salary) VALUES\n('John', 'Doe', 'Engineering', '2020-01-15', 85000),\n('Jane', 'Smith', 'Engineering', '2019-06-20', 92000),\n('Bob', 'Johnson', 'Sales', '2021-03-10', 75000);\n\nINSERT INTO projects (project_name, start_date, end_date, budget) VALUES\n('Project Alpha', '2025-01-01', '2025-06-30', 500000),\n('Project Beta', '2025-02-15', NULL, 750000),\n('Project Gamma', '2025-03-01', '2025-12-31', 300000);\n\nINSERT INTO project_assignments (employee_id, project_id, role, hours_allocated) VALUES\n(1, 1, 'Developer', 160),\n(1, 2, 'Lead Developer', 200),\n(2, 1, 'Architect', 120),\n(2, 2, 'Developer', 180),\n(2, 3, 'Consultant', 40);", "sql_prompt": "List all employees who are assigned to more than one project, showing their full name, department, and the number of projects they're working on.", "judge_passed": true, "failure_reason": "none", "judge_evaluations": [{"responses_create_params": {"background": null, "include": null, "input": [{"content": "You are an expert SQL judge evaluating the functional equivalence of two SQL queries. You will be given SQL query A and SQL query B. Your task is to determine whether both queries would produce identical result sets when executed against the provided database schema.\n\n## Core Evaluation Principle\n\nTwo SQL queries are functionally equivalent if and only if they return the same rows with the same column values for all possible database states conforming to the schema. Focus on semantic equivalence, not syntactic similarity.\n\n## SQL Equivalence Rules\n\n### Always Equivalent (ignore these differences):\n- **Formatting**: Whitespace, line breaks, capitalization of keywords\n- **Aliasing**: Different table/column aliases (e.g., `e.name` vs `employees.name`)\n- **JOIN syntax**: `FROM a, b WHERE a.id = b.id` \u2261 `FROM a JOIN b ON a.id = b.id`\n- **JOIN keyword**: `JOIN` \u2261 `INNER JOIN`\n- **Explicit vs implicit column lists**: When results are identical\n- **Redundant DISTINCT**: When the query already guarantees uniqueness (e.g., selecting a PRIMARY KEY)\n- **Parentheses**: Extra parentheses that don't change evaluation order\n- **Semicolons**: Trailing semicolon presence/absence\n\n### Equivalent Patterns (require careful analysis):\n- **CTE vs Subquery**: `WITH t AS (SELECT ...) SELECT * FROM t` may equal a nested subquery\n- **EXISTS vs IN vs JOIN**: Often equivalent for the same filtering logic\n- **COALESCE vs CASE**: `COALESCE(a, b)` \u2261 `CASE WHEN a IS NOT NULL THEN a ELSE b END`\n- **Boolean expressions**: `NOT (a AND b)` \u2261 `(NOT a) OR (NOT b)` (De Morgan's laws)\n- **Arithmetic**: `price * 1.1` \u2261 `price + price * 0.1`\n- **Date arithmetic**: Dialect-specific but logically equivalent date calculations\n\n### Never Equivalent (these always matter):\n- **Different columns selected**: `SELECT a, b` \u2260 `SELECT a, c`\n- **Different filtering**: `WHERE x > 5` \u2260 `WHERE x >= 5`\n- **Different aggregations**: `SUM(x)` \u2260 `AVG(x)`\n- **Different grouping**: `GROUP BY a` \u2260 `GROUP BY a, b`\n- **Missing/extra DISTINCT**: When it changes the result set\n- **Different ORDER BY**: When ordering is semantically required by the question\n- **Different LIMIT/OFFSET**: When they affect the result set\n- **NULL handling differences**: When they produce different results\n\n## Dialect-Specific Considerations\n\n### MySQL\n- Backtick quoting for identifiers: `table_name`.`column_name`\n- LIMIT syntax: `LIMIT n` or `LIMIT offset, count`\n- String functions: `SUBSTRING_INDEX()`, `CONCAT()`, `GROUP_CONCAT()`\n- Auto-increment: `AUTO_INCREMENT`\n- Boolean: `TINYINT(1)`, `TRUE`/`FALSE`\n\n### PostgreSQL\n- Double-quote identifiers: \"column\"\n- Type casting: `value::type` or `CAST(value AS type)`\n- Array operations: `ANY()`, `ALL()`, array literals\n- String functions: `SPLIT_PART()`, `STRING_AGG()`\n- Sequences: `SERIAL`, `BIGSERIAL`\n- Boolean: Native `BOOLEAN` type\n\n### SQLite\n- Limited type system (TEXT, INTEGER, REAL, BLOB)\n- String concatenation: `||` operator\n- No native BOOLEAN (uses 0/1)\n- `AUTOINCREMENT` keyword\n- No RIGHT JOIN or FULL OUTER JOIN\n\n## Output Format\n\nAnalyze both queries step by step, then provide your verdict:\n- If the queries are functionally equivalent: [[A=B]]\n- If the queries produce different results: [[A!=B]]\n\nExample: \"After analyzing both queries, my verdict is [[A=B]]\".", "role": "system", "type": "message"}, {"content": "<|SQL Dialect|>\nsqlite\n\n<|Database Schema and Sample Data|>\nCREATE TABLE employees (\n employee_id INTEGER PRIMARY KEY AUTOINCREMENT,\n first_name TEXT NOT NULL,\n last_name TEXT NOT NULL,\n department TEXT NOT NULL,\n hire_date TEXT NOT NULL,\n salary REAL NOT NULL,\n manager_id INTEGER REFERENCES employees(employee_id)\n);\n\nCREATE TABLE projects (\n project_id INTEGER PRIMARY KEY AUTOINCREMENT,\n project_name TEXT NOT NULL,\n start_date TEXT NOT NULL,\n end_date TEXT,\n budget REAL NOT NULL\n);\n\nCREATE TABLE project_assignments (\n assignment_id INTEGER PRIMARY KEY AUTOINCREMENT,\n employee_id INTEGER REFERENCES employees(employee_id),\n project_id INTEGER REFERENCES projects(project_id),\n role TEXT NOT NULL,\n hours_allocated INTEGER NOT NULL\n);\n\nINSERT INTO employees (first_name, last_name, department, hire_date, salary) VALUES\n('John', 'Doe', 'Engineering', '2020-01-15', 85000),\n('Jane', 'Smith', 'Engineering', '2019-06-20', 92000),\n('Bob', 'Johnson', 'Sales', '2021-03-10', 75000);\n\nINSERT INTO projects (project_name, start_date, end_date, budget) VALUES\n('Project Alpha', '2025-01-01', '2025-06-30', 500000),\n('Project Beta', '2025-02-15', NULL, 750000),\n('Project Gamma', '2025-03-01', '2025-12-31', 300000);\n\nINSERT INTO project_assignments (employee_id, project_id, role, hours_allocated) VALUES\n(1, 1, 'Developer', 160),\n(1, 2, 'Lead Developer', 200),\n(2, 1, 'Architect', 120),\n(2, 2, 'Developer', 180),\n(2, 3, 'Consultant', 40);\n\n<|Natural Language Question|>\nList all employees who are assigned to more than one project, showing their full name, department, and the number of projects they're working on.\n\n<|Start of SQL Query A|>\nSELECT\n e.first_name || ' ' || e.last_name AS full_name,\n e.department,\n COUNT(DISTINCT pa.project_id) AS project_count\nFROM employees e\nJOIN project_assignments pa ON e.employee_id = pa.employee_id\nGROUP BY e.employee_id, e.first_name, e.last_name, e.department\nHAVING COUNT(DISTINCT pa.project_id) > 1;\n<|End of SQL Query A|>\n\n<|Start of SQL Query B|>\nSELECT \n e.first_name || ' ' || e.last_name AS full_name,\n e.department,\n COUNT(DISTINCT pa.project_id) AS projects_count\nFROM \n employees e\nJOIN \n project_assignments pa ON e.employee_id = pa.employee_id\nGROUP BY \n e.employee_id, e.first_name, e.last_name, e.department\nHAVING \n COUNT(DISTINCT pa.project_id) > 1;\n<|End of SQL Query B|>", "role": "user", "type": "message"}], "instructions": null, "max_output_tokens": null, "max_tool_calls": null, "metadata": null, "model": null, "parallel_tool_calls": true, "previous_response_id": null, "prompt": null, "reasoning": null, "service_tier": null, "store": null, "temperature": null, "text": null, "tool_choice": "auto", "tools": [], "top_logprobs": null, "top_p": null, "truncation": null, "user": null, "stream": null}, "response": {"id": "resp_03cc44ec2da1332e006984e7fe366c8193aa5ac14fd6693cac", "created_at": 1770317822.0, "error": null, "incomplete_details": null, "instructions": null, "metadata": {}, "model": "gpt-4.1-2025-04-14", "object": "response", "output": [{"id": "msg_03cc44ec2da1332e006984e7febff0819380e644cf5b71b130", "content": [{"annotations": [], "text": "Let's analyze the queries step by step:\n\n### Query A\n\n```sql\nSELECT\n e.first_name || ' ' || e.last_name AS full_name,\n e.department,\n COUNT(DISTINCT pa.project_id) AS project_count\nFROM employees e\nJOIN project_assignments pa ON e.employee_id = pa.employee_id\nGROUP BY e.employee_id, e.first_name, e.last_name, e.department\nHAVING COUNT(DISTINCT pa.project_id) > 1;\n```\n\n- Joins `employees` with `project_assignments` on `employee_id`.\n- For each employee, counts the distinct number of projects they're assigned to.\n- Groups by employee (including all name and department columns for stability).\n- Uses `HAVING` to include only those with *more than one project assignment*.\n- Outputs: `full_name`, `department`, and number of distinct projects as `project_count`.\n\n---\n\n### Query B\n\n```sql\nSELECT \n e.first_name || ' ' || e.last_name AS full_name,\n e.department,\n COUNT(DISTINCT pa.project_id) AS projects_count\nFROM \n employees e\nJOIN \n project_assignments pa ON e.employee_id = pa.employee_id\nGROUP BY \n e.employee_id, e.first_name, e.last_name, e.department\nHAVING \n COUNT(DISTINCT pa.project_id) > 1;\n```\n\n- Structure, logic, joins, grouping, filtering, and even output expressions are identical to Query A.\n- The only difference is the alias for the last output column: `project_count` vs `projects_count`.\n\n---\n\n### Core Equivalence Consideration\n\n- **Column names/aliases:** In SQL, the alias for a column in the SELECT clause does *not* affect the actual data content or which rows are produced. It only changes the label for that column in the result set. Unless the question strictly requires a specific output label, the underlying result sets (rows and data values) are the same.\n- All else (grouping, HAVING, counting logic) is 100% identical.\n\n---\n\n### Verdict\n\nAfter analyzing both queries, my verdict is [[A=B]].", "type": "output_text", "logprobs": []}], "role": "assistant", "status": "completed", "type": "message"}], "parallel_tool_calls": true, "temperature": 1.0, "tool_choice": "auto", "tools": [], "top_p": 1.0, "background": false, "conversation": null, "max_output_tokens": null, "max_tool_calls": null, "previous_response_id": null, "prompt": null, "prompt_cache_key": null, "reasoning": {"effort": null, "generate_summary": null, "summary": null}, "safety_identifier": null, "service_tier": "default", "status": "completed", "text": {"format": {"type": "text"}, "verbosity": "medium"}, "top_logprobs": 0, "truncation": "disabled", "usage": {"input_tokens": 1520, "input_tokens_details": {"cached_tokens": 0}, "output_tokens": 444, "output_tokens_details": {"reasoning_tokens": 0}, "total_tokens": 1964}, "user": null, "billing": {"payer": "developer"}, "completed_at": 1770317829, "frequency_penalty": 0.0, "presence_penalty": 0.0, "prompt_cache_retention": null, "store": true}, "verdict_label": "[[A=B]]"}, {"responses_create_params": {"background": null, "include": null, "input": [{"content": "You are an expert SQL judge evaluating the functional equivalence of two SQL queries. You will be given SQL query A and SQL query B. Your task is to determine whether both queries would produce identical result sets when executed against the provided database schema.\n\n## Core Evaluation Principle\n\nTwo SQL queries are functionally equivalent if and only if they return the same rows with the same column values for all possible database states conforming to the schema. Focus on semantic equivalence, not syntactic similarity.\n\n## SQL Equivalence Rules\n\n### Always Equivalent (ignore these differences):\n- **Formatting**: Whitespace, line breaks, capitalization of keywords\n- **Aliasing**: Different table/column aliases (e.g., `e.name` vs `employees.name`)\n- **JOIN syntax**: `FROM a, b WHERE a.id = b.id` \u2261 `FROM a JOIN b ON a.id = b.id`\n- **JOIN keyword**: `JOIN` \u2261 `INNER JOIN`\n- **Explicit vs implicit column lists**: When results are identical\n- **Redundant DISTINCT**: When the query already guarantees uniqueness (e.g., selecting a PRIMARY KEY)\n- **Parentheses**: Extra parentheses that don't change evaluation order\n- **Semicolons**: Trailing semicolon presence/absence\n\n### Equivalent Patterns (require careful analysis):\n- **CTE vs Subquery**: `WITH t AS (SELECT ...) SELECT * FROM t` may equal a nested subquery\n- **EXISTS vs IN vs JOIN**: Often equivalent for the same filtering logic\n- **COALESCE vs CASE**: `COALESCE(a, b)` \u2261 `CASE WHEN a IS NOT NULL THEN a ELSE b END`\n- **Boolean expressions**: `NOT (a AND b)` \u2261 `(NOT a) OR (NOT b)` (De Morgan's laws)\n- **Arithmetic**: `price * 1.1` \u2261 `price + price * 0.1`\n- **Date arithmetic**: Dialect-specific but logically equivalent date calculations\n\n### Never Equivalent (these always matter):\n- **Different columns selected**: `SELECT a, b` \u2260 `SELECT a, c`\n- **Different filtering**: `WHERE x > 5` \u2260 `WHERE x >= 5`\n- **Different aggregations**: `SUM(x)` \u2260 `AVG(x)`\n- **Different grouping**: `GROUP BY a` \u2260 `GROUP BY a, b`\n- **Missing/extra DISTINCT**: When it changes the result set\n- **Different ORDER BY**: When ordering is semantically required by the question\n- **Different LIMIT/OFFSET**: When they affect the result set\n- **NULL handling differences**: When they produce different results\n\n## Dialect-Specific Considerations\n\n### MySQL\n- Backtick quoting for identifiers: `table_name`.`column_name`\n- LIMIT syntax: `LIMIT n` or `LIMIT offset, count`\n- String functions: `SUBSTRING_INDEX()`, `CONCAT()`, `GROUP_CONCAT()`\n- Auto-increment: `AUTO_INCREMENT`\n- Boolean: `TINYINT(1)`, `TRUE`/`FALSE`\n\n### PostgreSQL\n- Double-quote identifiers: \"column\"\n- Type casting: `value::type` or `CAST(value AS type)`\n- Array operations: `ANY()`, `ALL()`, array literals\n- String functions: `SPLIT_PART()`, `STRING_AGG()`\n- Sequences: `SERIAL`, `BIGSERIAL`\n- Boolean: Native `BOOLEAN` type\n\n### SQLite\n- Limited type system (TEXT, INTEGER, REAL, BLOB)\n- String concatenation: `||` operator\n- No native BOOLEAN (uses 0/1)\n- `AUTOINCREMENT` keyword\n- No RIGHT JOIN or FULL OUTER JOIN\n\n## Output Format\n\nAnalyze both queries step by step, then provide your verdict:\n- If the queries are functionally equivalent: [[A=B]]\n- If the queries produce different results: [[A!=B]]\n\nExample: \"After analyzing both queries, my verdict is [[A=B]]\".", "role": "system", "type": "message"}, {"content": "<|SQL Dialect|>\nsqlite\n\n<|Database Schema and Sample Data|>\nCREATE TABLE employees (\n employee_id INTEGER PRIMARY KEY AUTOINCREMENT,\n first_name TEXT NOT NULL,\n last_name TEXT NOT NULL,\n department TEXT NOT NULL,\n hire_date TEXT NOT NULL,\n salary REAL NOT NULL,\n manager_id INTEGER REFERENCES employees(employee_id)\n);\n\nCREATE TABLE projects (\n project_id INTEGER PRIMARY KEY AUTOINCREMENT,\n project_name TEXT NOT NULL,\n start_date TEXT NOT NULL,\n end_date TEXT,\n budget REAL NOT NULL\n);\n\nCREATE TABLE project_assignments (\n assignment_id INTEGER PRIMARY KEY AUTOINCREMENT,\n employee_id INTEGER REFERENCES employees(employee_id),\n project_id INTEGER REFERENCES projects(project_id),\n role TEXT NOT NULL,\n hours_allocated INTEGER NOT NULL\n);\n\nINSERT INTO employees (first_name, last_name, department, hire_date, salary) VALUES\n('John', 'Doe', 'Engineering', '2020-01-15', 85000),\n('Jane', 'Smith', 'Engineering', '2019-06-20', 92000),\n('Bob', 'Johnson', 'Sales', '2021-03-10', 75000);\n\nINSERT INTO projects (project_name, start_date, end_date, budget) VALUES\n('Project Alpha', '2025-01-01', '2025-06-30', 500000),\n('Project Beta', '2025-02-15', NULL, 750000),\n('Project Gamma', '2025-03-01', '2025-12-31', 300000);\n\nINSERT INTO project_assignments (employee_id, project_id, role, hours_allocated) VALUES\n(1, 1, 'Developer', 160),\n(1, 2, 'Lead Developer', 200),\n(2, 1, 'Architect', 120),\n(2, 2, 'Developer', 180),\n(2, 3, 'Consultant', 40);\n\n<|Natural Language Question|>\nList all employees who are assigned to more than one project, showing their full name, department, and the number of projects they're working on.\n\n<|Start of SQL Query A|>\nSELECT \n e.first_name || ' ' || e.last_name AS full_name,\n e.department,\n COUNT(DISTINCT pa.project_id) AS projects_count\nFROM \n employees e\nJOIN \n project_assignments pa ON e.employee_id = pa.employee_id\nGROUP BY \n e.employee_id, e.first_name, e.last_name, e.department\nHAVING \n COUNT(DISTINCT pa.project_id) > 1;\n<|End of SQL Query A|>\n\n<|Start of SQL Query B|>\nSELECT\n e.first_name || ' ' || e.last_name AS full_name,\n e.department,\n COUNT(DISTINCT pa.project_id) AS project_count\nFROM employees e\nJOIN project_assignments pa ON e.employee_id = pa.employee_id\nGROUP BY e.employee_id, e.first_name, e.last_name, e.department\nHAVING COUNT(DISTINCT pa.project_id) > 1;\n<|End of SQL Query B|>", "role": "user", "type": "message"}], "instructions": null, "max_output_tokens": null, "max_tool_calls": null, "metadata": null, "model": null, "parallel_tool_calls": true, "previous_response_id": null, "prompt": null, "reasoning": null, "service_tier": null, "store": null, "temperature": null, "text": null, "tool_choice": "auto", "tools": [], "top_logprobs": null, "top_p": null, "truncation": null, "user": null, "stream": null}, "response": {"id": "resp_02425373efc964b4006984e80552a08197b851daddf5a8512f", "created_at": 1770317829.0, "error": null, "incomplete_details": null, "instructions": null, "metadata": {}, "model": "gpt-4.1-2025-04-14", "object": "response", "output": [{"id": "msg_02425373efc964b4006984e805c2d48197ab18c0c194a465b1", "content": [{"annotations": [], "text": "Let's analyze both Query A and Query B step by step:\n\n## Query A\n```sql\nSELECT \n e.first_name || ' ' || e.last_name AS full_name,\n e.department,\n COUNT(DISTINCT pa.project_id) AS projects_count\nFROM \n employees e\nJOIN \n project_assignments pa ON e.employee_id = pa.employee_id\nGROUP BY \n e.employee_id, e.first_name, e.last_name, e.department\nHAVING \n COUNT(DISTINCT pa.project_id) > 1;\n```\n- Selects full name (concatenation), department, and the count of distinct projects for each employee.\n- Groups by employee identity and info.\n- Filters to only those with more than one project.\n\n## Query B\n```sql\nSELECT\n e.first_name || ' ' || e.last_name AS full_name,\n e.department,\n COUNT(DISTINCT pa.project_id) AS project_count\nFROM employees e\nJOIN project_assignments pa ON e.employee_id = pa.employee_id\nGROUP BY e.employee_id, e.first_name, e.last_name, e.department\nHAVING COUNT(DISTINCT pa.project_id) > 1;\n```\n- Identical logic:\n - Selects full name, department, and count of distinct projects.\n - Counts column alias is `project_count` instead of `projects_count` in Query A.\n- GROUP BY and HAVING clauses are identical.\n\n## Compare for Equivalence\n\n### Differences:\n- Only the output column alias for the third column differs: `projects_count` vs. `project_count`.\n- In SQLite, column aliases only affect the output result's column names\u2014they do not affect row selection, calculation, filtering, grouping, or any logic.\n- The actual values and row selection logic are identical.\n\n### Functional Equivalence\n- For all possible data, the actual result set (rows and values) is the same in both queries; only the label for the column with the number of projects differs.\n- Unless the column alias itself is required to be functionally equivalent for some downstream logic (which is not the case per your equivalence principle), both queries return the same data.\n\n## Verdict\n\nAfter analyzing both queries, my verdict is [[A=B]].", "type": "output_text", "logprobs": []}], "role": "assistant", "status": "completed", "type": "message"}], "parallel_tool_calls": true, "temperature": 1.0, "tool_choice": "auto", "tools": [], "top_p": 1.0, "background": false, "conversation": null, "max_output_tokens": null, "max_tool_calls": null, "previous_response_id": null, "prompt": null, "prompt_cache_key": null, "reasoning": {"effort": null, "generate_summary": null, "summary": null}, "safety_identifier": null, "service_tier": "default", "status": "completed", "text": {"format": {"type": "text"}, "verbosity": "medium"}, "top_logprobs": 0, "truncation": "disabled", "usage": {"input_tokens": 1520, "input_tokens_details": {"cached_tokens": 1024}, "output_tokens": 460, "output_tokens_details": {"reasoning_tokens": 0}, "total_tokens": 1980}, "user": null, "billing": {"payer": "developer"}, "completed_at": 1770317838, "frequency_penalty": 0.0, "presence_penalty": 0.0, "prompt_cache_retention": null, "store": true}, "verdict_label": "[[A=B]]"}], "metadata": null} -{"responses_create_params": {"background": null, "include": null, "input": [{"content": "You are a SQL assistant who expertly maps natural language commands and problems into clean, efficient, and well-structured SQL queries. You always carefully examine the database context to include only the relevant tables and fields. You return only SQL code (no explanations outside of SQL code block; only proper comments are permitted). Wrap your SQL query inside a ```sql``` code block.", "role": "system", "type": "message"}, {"content": "DIALECT: mysql\n\nDATABASE CONTEXT:\nCREATE TABLE products (\n product_id INT UNSIGNED AUTO_INCREMENT PRIMARY KEY,\n product_name VARCHAR(200) NOT NULL,\n category VARCHAR(50) NOT NULL,\n price DECIMAL(10,2) NOT NULL,\n stock_quantity INT NOT NULL DEFAULT 0,\n created_at DATETIME DEFAULT CURRENT_TIMESTAMP\n) ENGINE=InnoDB;\n\nCREATE TABLE sales (\n sale_id INT UNSIGNED AUTO_INCREMENT PRIMARY KEY,\n product_id INT UNSIGNED NOT NULL,\n sale_date DATE NOT NULL,\n quantity_sold INT NOT NULL,\n revenue DECIMAL(10,2) NOT NULL,\n FOREIGN KEY (product_id) REFERENCES products(product_id)\n) ENGINE=InnoDB;\n\nINSERT INTO products (product_name, category, price, stock_quantity) VALUES\n('Laptop Pro', 'Electronics', 1299.99, 50),\n('Wireless Mouse', 'Electronics', 29.99, 200),\n('Office Chair', 'Furniture', 249.99, 75),\n('Standing Desk', 'Furniture', 599.99, 30),\n('USB Hub', 'Electronics', 39.99, 150);\n\nINSERT INTO sales (product_id, sale_date, quantity_sold, revenue) VALUES\n(1, '2025-01-15', 10, 12999.90),\n(2, '2025-01-15', 50, 1499.50),\n(1, '2025-02-10', 8, 10399.92),\n(3, '2025-02-15', 20, 4999.80),\n(4, '2025-02-20', 5, 2999.95),\n(2, '2025-03-01', 100, 2999.00),\n(5, '2025-03-05', 75, 2999.25);\n\nQUESTION:\nFor each product category, find the top-selling product (by total quantity sold) and show the category, product name, and total units sold.", "role": "user", "type": "message"}], "instructions": null, "max_output_tokens": null, "max_tool_calls": null, "metadata": null, "model": null, "parallel_tool_calls": true, "previous_response_id": null, "prompt": null, "reasoning": null, "service_tier": null, "store": null, "temperature": null, "text": null, "tool_choice": "auto", "tools": [], "top_logprobs": null, "top_p": null, "truncation": null, "user": null, "stream": null}, "response": {"id": "resp_04a0bcc19e3b81e6006984e7fc70488191a523959ddf8a6d3b", "created_at": 1770317820.0, "error": null, "incomplete_details": null, "instructions": null, "metadata": {}, "model": "gpt-4.1-2025-04-14", "object": "response", "output": [{"id": "msg_04a0bcc19e3b81e6006984e7fce7408191875cc08011806209", "content": [{"annotations": [], "text": "```sql\nSELECT\n p.category,\n p.product_name,\n SUM(s.quantity_sold) AS total_units_sold\nFROM\n products p\nJOIN\n sales s ON p.product_id = s.product_id\nGROUP BY\n p.product_id, p.category, p.product_name\nHAVING\n (p.category, SUM(s.quantity_sold)) IN (\n SELECT \n p2.category, MAX(total_sold)\n FROM \n (SELECT p2.category, p2.product_id, SUM(s2.quantity_sold) AS total_sold\n FROM products p2\n JOIN sales s2 ON p2.product_id = s2.product_id\n GROUP BY p2.category, p2.product_id) AS cat_sales\n GROUP BY p2.category\n )\nORDER BY\n p.category;\n```", "type": "output_text", "logprobs": []}], "role": "assistant", "status": "completed", "type": "message"}], "parallel_tool_calls": true, "temperature": 1.0, "tool_choice": "auto", "tools": [], "top_p": 1.0, "background": false, "conversation": null, "max_output_tokens": null, "max_tool_calls": null, "previous_response_id": null, "prompt": null, "prompt_cache_key": null, "reasoning": {"effort": null, "generate_summary": null, "summary": null}, "safety_identifier": null, "service_tier": "default", "status": "completed", "text": {"format": {"type": "text"}, "verbosity": "medium"}, "top_logprobs": 0, "truncation": "disabled", "usage": {"input_tokens": 520, "input_tokens_details": {"cached_tokens": 0}, "output_tokens": 174, "output_tokens_details": {"reasoning_tokens": 0}, "total_tokens": 694}, "user": null, "billing": {"payer": "developer"}, "completed_at": 1770317825, "frequency_penalty": 0.0, "presence_penalty": 0.0, "prompt_cache_retention": null, "store": true}, "reward": 0.0, "uuid": "sales-analytics-001", "sql": "WITH category_sales AS (\n SELECT\n p.category,\n p.product_id,\n p.product_name,\n SUM(s.quantity_sold) AS total_sold,\n ROW_NUMBER() OVER (PARTITION BY p.category ORDER BY SUM(s.quantity_sold) DESC) AS rn\n FROM products p\n JOIN sales s ON p.product_id = s.product_id\n GROUP BY p.category, p.product_id, p.product_name\n)\nSELECT category, product_name, total_sold\nFROM category_sales\nWHERE rn = 1;", "model_output": "```sql\nSELECT\n p.category,\n p.product_name,\n SUM(s.quantity_sold) AS total_units_sold\nFROM\n products p\nJOIN\n sales s ON p.product_id = s.product_id\nGROUP BY\n p.product_id, p.category, p.product_name\nHAVING\n (p.category, SUM(s.quantity_sold)) IN (\n SELECT \n p2.category, MAX(total_sold)\n FROM \n (SELECT p2.category, p2.product_id, SUM(s2.quantity_sold) AS total_sold\n FROM products p2\n JOIN sales s2 ON p2.product_id = s2.product_id\n GROUP BY p2.category, p2.product_id) AS cat_sales\n GROUP BY p2.category\n )\nORDER BY\n p.category;\n```", "extracted_sql": "SELECT\n p.category,\n p.product_name,\n SUM(s.quantity_sold) AS total_units_sold\nFROM\n products p\nJOIN\n sales s ON p.product_id = s.product_id\nGROUP BY\n p.product_id, p.category, p.product_name\nHAVING\n (p.category, SUM(s.quantity_sold)) IN (\n SELECT \n p2.category, MAX(total_sold)\n FROM \n (SELECT p2.category, p2.product_id, SUM(s2.quantity_sold) AS total_sold\n FROM products p2\n JOIN sales s2 ON p2.product_id = s2.product_id\n GROUP BY p2.category, p2.product_id) AS cat_sales\n GROUP BY p2.category\n )\nORDER BY\n p.category;", "sql_dialect": "mysql", "sql_context": "CREATE TABLE products (\n product_id INT UNSIGNED AUTO_INCREMENT PRIMARY KEY,\n product_name VARCHAR(200) NOT NULL,\n category VARCHAR(50) NOT NULL,\n price DECIMAL(10,2) NOT NULL,\n stock_quantity INT NOT NULL DEFAULT 0,\n created_at DATETIME DEFAULT CURRENT_TIMESTAMP\n) ENGINE=InnoDB;\n\nCREATE TABLE sales (\n sale_id INT UNSIGNED AUTO_INCREMENT PRIMARY KEY,\n product_id INT UNSIGNED NOT NULL,\n sale_date DATE NOT NULL,\n quantity_sold INT NOT NULL,\n revenue DECIMAL(10,2) NOT NULL,\n FOREIGN KEY (product_id) REFERENCES products(product_id)\n) ENGINE=InnoDB;\n\nINSERT INTO products (product_name, category, price, stock_quantity) VALUES\n('Laptop Pro', 'Electronics', 1299.99, 50),\n('Wireless Mouse', 'Electronics', 29.99, 200),\n('Office Chair', 'Furniture', 249.99, 75),\n('Standing Desk', 'Furniture', 599.99, 30),\n('USB Hub', 'Electronics', 39.99, 150);\n\nINSERT INTO sales (product_id, sale_date, quantity_sold, revenue) VALUES\n(1, '2025-01-15', 10, 12999.90),\n(2, '2025-01-15', 50, 1499.50),\n(1, '2025-02-10', 8, 10399.92),\n(3, '2025-02-15', 20, 4999.80),\n(4, '2025-02-20', 5, 2999.95),\n(2, '2025-03-01', 100, 2999.00),\n(5, '2025-03-05', 75, 2999.25);", "sql_prompt": "For each product category, find the top-selling product (by total quantity sold) and show the category, product name, and total units sold.", "judge_passed": false, "failure_reason": "judge_evaluation_failed", "judge_evaluations": [{"responses_create_params": {"background": null, "include": null, "input": [{"content": "You are an expert SQL judge evaluating the functional equivalence of two SQL queries. You will be given SQL query A and SQL query B. Your task is to determine whether both queries would produce identical result sets when executed against the provided database schema.\n\n## Core Evaluation Principle\n\nTwo SQL queries are functionally equivalent if and only if they return the same rows with the same column values for all possible database states conforming to the schema. Focus on semantic equivalence, not syntactic similarity.\n\n## SQL Equivalence Rules\n\n### Always Equivalent (ignore these differences):\n- **Formatting**: Whitespace, line breaks, capitalization of keywords\n- **Aliasing**: Different table/column aliases (e.g., `e.name` vs `employees.name`)\n- **JOIN syntax**: `FROM a, b WHERE a.id = b.id` \u2261 `FROM a JOIN b ON a.id = b.id`\n- **JOIN keyword**: `JOIN` \u2261 `INNER JOIN`\n- **Explicit vs implicit column lists**: When results are identical\n- **Redundant DISTINCT**: When the query already guarantees uniqueness (e.g., selecting a PRIMARY KEY)\n- **Parentheses**: Extra parentheses that don't change evaluation order\n- **Semicolons**: Trailing semicolon presence/absence\n\n### Equivalent Patterns (require careful analysis):\n- **CTE vs Subquery**: `WITH t AS (SELECT ...) SELECT * FROM t` may equal a nested subquery\n- **EXISTS vs IN vs JOIN**: Often equivalent for the same filtering logic\n- **COALESCE vs CASE**: `COALESCE(a, b)` \u2261 `CASE WHEN a IS NOT NULL THEN a ELSE b END`\n- **Boolean expressions**: `NOT (a AND b)` \u2261 `(NOT a) OR (NOT b)` (De Morgan's laws)\n- **Arithmetic**: `price * 1.1` \u2261 `price + price * 0.1`\n- **Date arithmetic**: Dialect-specific but logically equivalent date calculations\n\n### Never Equivalent (these always matter):\n- **Different columns selected**: `SELECT a, b` \u2260 `SELECT a, c`\n- **Different filtering**: `WHERE x > 5` \u2260 `WHERE x >= 5`\n- **Different aggregations**: `SUM(x)` \u2260 `AVG(x)`\n- **Different grouping**: `GROUP BY a` \u2260 `GROUP BY a, b`\n- **Missing/extra DISTINCT**: When it changes the result set\n- **Different ORDER BY**: When ordering is semantically required by the question\n- **Different LIMIT/OFFSET**: When they affect the result set\n- **NULL handling differences**: When they produce different results\n\n## Dialect-Specific Considerations\n\n### MySQL\n- Backtick quoting for identifiers: `table_name`.`column_name`\n- LIMIT syntax: `LIMIT n` or `LIMIT offset, count`\n- String functions: `SUBSTRING_INDEX()`, `CONCAT()`, `GROUP_CONCAT()`\n- Auto-increment: `AUTO_INCREMENT`\n- Boolean: `TINYINT(1)`, `TRUE`/`FALSE`\n\n### PostgreSQL\n- Double-quote identifiers: \"column\"\n- Type casting: `value::type` or `CAST(value AS type)`\n- Array operations: `ANY()`, `ALL()`, array literals\n- String functions: `SPLIT_PART()`, `STRING_AGG()`\n- Sequences: `SERIAL`, `BIGSERIAL`\n- Boolean: Native `BOOLEAN` type\n\n### SQLite\n- Limited type system (TEXT, INTEGER, REAL, BLOB)\n- String concatenation: `||` operator\n- No native BOOLEAN (uses 0/1)\n- `AUTOINCREMENT` keyword\n- No RIGHT JOIN or FULL OUTER JOIN\n\n## Output Format\n\nAnalyze both queries step by step, then provide your verdict:\n- If the queries are functionally equivalent: [[A=B]]\n- If the queries produce different results: [[A!=B]]\n\nExample: \"After analyzing both queries, my verdict is [[A=B]]\".", "role": "system", "type": "message"}, {"content": "<|SQL Dialect|>\nmysql\n\n<|Database Schema and Sample Data|>\nCREATE TABLE products (\n product_id INT UNSIGNED AUTO_INCREMENT PRIMARY KEY,\n product_name VARCHAR(200) NOT NULL,\n category VARCHAR(50) NOT NULL,\n price DECIMAL(10,2) NOT NULL,\n stock_quantity INT NOT NULL DEFAULT 0,\n created_at DATETIME DEFAULT CURRENT_TIMESTAMP\n) ENGINE=InnoDB;\n\nCREATE TABLE sales (\n sale_id INT UNSIGNED AUTO_INCREMENT PRIMARY KEY,\n product_id INT UNSIGNED NOT NULL,\n sale_date DATE NOT NULL,\n quantity_sold INT NOT NULL,\n revenue DECIMAL(10,2) NOT NULL,\n FOREIGN KEY (product_id) REFERENCES products(product_id)\n) ENGINE=InnoDB;\n\nINSERT INTO products (product_name, category, price, stock_quantity) VALUES\n('Laptop Pro', 'Electronics', 1299.99, 50),\n('Wireless Mouse', 'Electronics', 29.99, 200),\n('Office Chair', 'Furniture', 249.99, 75),\n('Standing Desk', 'Furniture', 599.99, 30),\n('USB Hub', 'Electronics', 39.99, 150);\n\nINSERT INTO sales (product_id, sale_date, quantity_sold, revenue) VALUES\n(1, '2025-01-15', 10, 12999.90),\n(2, '2025-01-15', 50, 1499.50),\n(1, '2025-02-10', 8, 10399.92),\n(3, '2025-02-15', 20, 4999.80),\n(4, '2025-02-20', 5, 2999.95),\n(2, '2025-03-01', 100, 2999.00),\n(5, '2025-03-05', 75, 2999.25);\n\n<|Natural Language Question|>\nFor each product category, find the top-selling product (by total quantity sold) and show the category, product name, and total units sold.\n\n<|Start of SQL Query A|>\nWITH category_sales AS (\n SELECT\n p.category,\n p.product_id,\n p.product_name,\n SUM(s.quantity_sold) AS total_sold,\n ROW_NUMBER() OVER (PARTITION BY p.category ORDER BY SUM(s.quantity_sold) DESC) AS rn\n FROM products p\n JOIN sales s ON p.product_id = s.product_id\n GROUP BY p.category, p.product_id, p.product_name\n)\nSELECT category, product_name, total_sold\nFROM category_sales\nWHERE rn = 1;\n<|End of SQL Query A|>\n\n<|Start of SQL Query B|>\nSELECT\n p.category,\n p.product_name,\n SUM(s.quantity_sold) AS total_units_sold\nFROM\n products p\nJOIN\n sales s ON p.product_id = s.product_id\nGROUP BY\n p.product_id, p.category, p.product_name\nHAVING\n (p.category, SUM(s.quantity_sold)) IN (\n SELECT \n p2.category, MAX(total_sold)\n FROM \n (SELECT p2.category, p2.product_id, SUM(s2.quantity_sold) AS total_sold\n FROM products p2\n JOIN sales s2 ON p2.product_id = s2.product_id\n GROUP BY p2.category, p2.product_id) AS cat_sales\n GROUP BY p2.category\n )\nORDER BY\n p.category;\n<|End of SQL Query B|>", "role": "user", "type": "message"}], "instructions": null, "max_output_tokens": null, "max_tool_calls": null, "metadata": null, "model": null, "parallel_tool_calls": true, "previous_response_id": null, "prompt": null, "reasoning": null, "service_tier": null, "store": null, "temperature": null, "text": null, "tool_choice": "auto", "tools": [], "top_logprobs": null, "top_p": null, "truncation": null, "user": null, "stream": null}, "response": {"id": "resp_026e86699a7c2a64006984e802258c81908f4fd5ac2f91c9a7", "created_at": 1770317826.0, "error": null, "incomplete_details": null, "instructions": null, "metadata": {}, "model": "gpt-4.1-2025-04-14", "object": "response", "output": [{"id": "msg_026e86699a7c2a64006984e802c80081908f2a2125d2ac71f6", "content": [{"annotations": [], "text": "Let's analyze both queries step by step for **the question**: \n*For each product category, find the top-selling product (by total quantity sold) and show the category, product name, and total units sold.*\n\n---\n\n### **Query A Analysis**\n\n**Step 1: CTE (`category_sales`)**\n\nFor each product in each category:\n- Joins `products` to `sales` (only products with sales).\n- Sums `s.quantity_sold` (total units sold per product).\n- Uses `ROW_NUMBER() OVER (PARTITION BY p.category ORDER BY SUM(s.quantity_sold) DESC)` to rank products within each category by their total sold (highest = 1).\n\n**Step 2: Outer Query**\n- Selects `category`, `product_name`, and `total_sold` for rows where `rn = 1` (the top-seller of each category).\n- **If there's a tie**, i.e., two products have the same highest units sold in a category, **only one** will be returned (the one ranked first by `ROW_NUMBER()`, which in MySQL depends on the arbitrary sort order of ties unless further ORDER BY specified).\n\n---\n\n### **Query B Analysis**\n\n**Step 1: Main SELECT**\n- Joins `products` to `sales`, groups by `p.product_id`, `p.category`, `p.product_name`.\n- Aggregates `SUM(s.quantity_sold)` as `total_units_sold`.\n\n**Step 2: HAVING filter**\n- Keeps only those groups where `(p.category, SUM(s.quantity_sold))` pair exists in:\n - A subquery: for each category, finds the **maximum total sold** per category (by aggregating in a derived table and then `MAX(total_sold)` per category).\n\n**Key points:**\n- If **multiple products** in a category have the same maximum total sold, **all will be returned** (because the IN clause will match all products within the category with that value).\n- Output columns: category, product_name, total_units_sold (identical logic and meaning to A, except possibly in tie situations).\n\n---\n\n### **Comparing for Functional Equivalence**\n\n#### **Column Selection**\n- Both select category, product name, and total units sold (possibly with differing aliases).\n\n#### **Aggregation Scope**\n- Both aggregate sum of sold quantities per product per category.\n\n#### **Top Seller Selection Logic**\n- **Query A**: With `ROW_NUMBER()`, if a tie exists for the highest sold, **only one** product (arbitrary in MySQL) will be returned for the category.\n- **Query B**: Will return **all** tied products (since each with the max total per category passes the `(category, MAX(total_sold))` IN clause).\n\n#### **Tie Handling Difference**\n- If there is a **tie** for maximal `quantity_sold` in a category, Query A returns only one row (arbitrary among those tied), while Query B returns **all** tied products. \n- If there are **no ties** in any category, the outputs are identical.\n\n#### **Conclusion**\n\nThe queries are **not functionally equivalent** for the case of ties on `total units sold` within a category.\n\n---\n\n**Example counter-case (with ties):**\n\nSuppose in 'Electronics', two products both have a `SUM(s.quantity_sold)` of 100. \n- Query A (using `ROW_NUMBER()`): returns **only one** of them, arbitrarily.\n- Query B (using `IN` with the maximum): returns **both**.\n\n---\n\n**Final Verdict:**\n\nAfter analyzing both queries, my verdict is **[[A!=B]]**. \n*They are not functionally equivalent due to different tie-handling logic. Query B correctly returns all top-selling products in the case of ties, but Query A only one. This answers the natural language request more completely.*", "type": "output_text", "logprobs": []}], "role": "assistant", "status": "completed", "type": "message"}], "parallel_tool_calls": true, "temperature": 1.0, "tool_choice": "auto", "tools": [], "top_p": 1.0, "background": false, "conversation": null, "max_output_tokens": null, "max_tool_calls": null, "previous_response_id": null, "prompt": null, "prompt_cache_key": null, "reasoning": {"effort": null, "generate_summary": null, "summary": null}, "safety_identifier": null, "service_tier": "default", "status": "completed", "text": {"format": {"type": "text"}, "verbosity": "medium"}, "top_logprobs": 0, "truncation": "disabled", "usage": {"input_tokens": 1629, "input_tokens_details": {"cached_tokens": 0}, "output_tokens": 805, "output_tokens_details": {"reasoning_tokens": 0}, "total_tokens": 2434}, "user": null, "billing": {"payer": "developer"}, "completed_at": 1770317848, "frequency_penalty": 0.0, "presence_penalty": 0.0, "prompt_cache_retention": null, "store": true}, "verdict_label": "[[A!=B]]"}], "metadata": null} -{"responses_create_params": {"background": null, "include": null, "input": [{"content": "You are a SQL assistant who expertly maps natural language commands and problems into clean, efficient, and well-structured SQL queries. You always carefully examine the database context to include only the relevant tables and fields. You return only SQL code (no explanations outside of SQL code block; only proper comments are permitted). Wrap your SQL query inside a ```sql``` code block.", "role": "system", "type": "message"}, {"content": "DIALECT: mysql\n\nDATABASE CONTEXT:\nCREATE TABLE weather_station (\n station_id INT UNSIGNED AUTO_INCREMENT PRIMARY KEY,\n station_name VARCHAR(100) NOT NULL,\n latitude DECIMAL(9,6) NOT NULL,\n longitude DECIMAL(9,6) NOT NULL,\n installed_date DATE NOT NULL,\n created_at DATETIME NOT NULL,\n updated_at DATETIME NOT NULL,\n is_active TINYINT(1) NOT NULL DEFAULT 1,\n description TEXT\n) ENGINE=InnoDB;\n\nCREATE TABLE sensor (\n sensor_id INT UNSIGNED AUTO_INCREMENT PRIMARY KEY,\n station_id INT UNSIGNED NOT NULL,\n sensor_type VARCHAR(50) NOT NULL,\n calibration_date DATE NOT NULL,\n serial_number VARCHAR(50),\n firmware_version VARCHAR(20),\n is_active TINYINT(1) NOT NULL DEFAULT 1,\n created_at DATETIME NOT NULL,\n updated_at DATETIME NOT NULL,\n description TEXT,\n FOREIGN KEY (station_id) REFERENCES weather_station(station_id)\n) ENGINE=InnoDB;\n\nCREATE TABLE sensor_log (\n log_id BIGINT UNSIGNED AUTO_INCREMENT PRIMARY KEY,\n sensor_id INT UNSIGNED NOT NULL,\n drift_date DATE NOT NULL,\n temperature_deviation DECIMAL(5,2) NOT NULL,\n reporting_email VARCHAR(150) NOT NULL,\n log_url VARCHAR(255) NOT NULL,\n readings TEXT NOT NULL,\n created_at DATETIME NOT NULL,\n updated_at DATETIME NOT NULL,\n is_processed TINYINT(1) NOT NULL DEFAULT 0,\n notes TEXT,\n FOREIGN KEY (sensor_id) REFERENCES sensor(sensor_id)\n) ENGINE=InnoDB;\n\nINSERT INTO weather_station (station_name, latitude, longitude, installed_date, created_at, updated_at, is_active, description) VALUES\n('North Ridge', 45.123456, -122.654321, '2023-03-15', '2023-04-01 08:00:00', '2023-04-01 08:00:00', 1, 'High elevation site'),\n('East Valley', 44.987654, -122.112233, '2022-11-20', '2022-12-01 09:15:00', '2022-12-01 09:15:00', 1, NULL);\n\nINSERT INTO sensor (station_id, sensor_type, calibration_date, serial_number, firmware_version, is_active, created_at, updated_at, description) VALUES\n(1, 'Thermometer', '2025-01-01', 'SN-001', 'v1.2.0', 1, '2025-01-02 08:00:00', '2025-01-02 08:00:00', NULL),\n(2, 'Thermometer', '2024-12-10', 'SN-003', 'v1.1.3', 1, '2024-12-12 09:00:00', '2024-12-12 09:00:00', NULL);\n\nINSERT INTO sensor_log (sensor_id, drift_date, temperature_deviation, reporting_email, log_url, readings, created_at, updated_at, is_processed, notes) VALUES\n(1, '2025-07-01', 0.45, 'tech.north@example.com', 'http://logs.example.com/north/2025-07-01', '[21.2,21.5,21.7]', '2025-07-02 08:10:00', '2025-07-02 08:10:00', 1, NULL),\n(1, '2025-07-15', 0.30, 'ops.north@example.com', 'http://logs.example.com/north/2025-07-15', '[21.0,21.1]', '2025-07-16 08:12:00', '2025-07-16 08:12:00', 0, NULL),\n(2, '2025-07-03', -0.20, 'maintenance@eastvalley.org', 'http://logs.example.com/east/2025-07-03', '[19.8,20.0,20.2,20.3]', '2025-07-04 09:00:00', '2025-07-04 09:00:00', 1, NULL);\n\nQUESTION:\nPlease retrieve for each weather station the latest sensor-drift date, the average temperature deviation, and the domain extracted from the reporting email within the sensor log URL, omitting any records whose embedded readings array contains fewer than three entries, and return the list ordered by descending average deviation.", "role": "user", "type": "message"}], "instructions": null, "max_output_tokens": null, "max_tool_calls": null, "metadata": null, "model": null, "parallel_tool_calls": true, "previous_response_id": null, "prompt": null, "reasoning": null, "service_tier": null, "store": null, "temperature": null, "text": null, "tool_choice": "auto", "tools": [], "top_logprobs": null, "top_p": null, "truncation": null, "user": null, "stream": null}, "response": {"id": "resp_05547656757a2aa5006984e7fc68cc81a1bb571619e4b42dbc", "created_at": 1770317820.0, "error": null, "incomplete_details": null, "instructions": null, "metadata": {}, "model": "gpt-4.1-2025-04-14", "object": "response", "output": [{"id": "msg_05547656757a2aa5006984e7fd34a881a1a6e2a025a35a56cd", "content": [{"annotations": [], "text": "```sql\nSELECT\n ws.station_id,\n ws.station_name,\n MAX(sl.drift_date) AS latest_drift_date,\n AVG(sl.temperature_deviation) AS avg_temperature_deviation,\n SUBSTRING_INDEX(SUBSTRING_INDEX(sl.reporting_email, '@', -1), '.', -2) AS email_domain\nFROM\n weather_station ws\n JOIN sensor s ON ws.station_id = s.station_id\n JOIN sensor_log sl ON s.sensor_id = sl.sensor_id\nWHERE\n -- Ensure readings array has at least three entries\n (LENGTH(sl.readings) - LENGTH(REPLACE(sl.readings, ',', '')) + 1) >= 3\nGROUP BY\n ws.station_id,\n ws.station_name,\n SUBSTRING_INDEX(SUBSTRING_INDEX(sl.reporting_email, '@', -1), '.', -2)\nORDER BY\n avg_temperature_deviation DESC;\n```\n", "type": "output_text", "logprobs": []}], "role": "assistant", "status": "completed", "type": "message"}], "parallel_tool_calls": true, "temperature": 1.0, "tool_choice": "auto", "tools": [], "top_p": 1.0, "background": false, "conversation": null, "max_output_tokens": null, "max_tool_calls": null, "previous_response_id": null, "prompt": null, "prompt_cache_key": null, "reasoning": {"effort": null, "generate_summary": null, "summary": null}, "safety_identifier": null, "service_tier": "default", "status": "completed", "text": {"format": {"type": "text"}, "verbosity": "medium"}, "top_logprobs": 0, "truncation": "disabled", "usage": {"input_tokens": 1082, "input_tokens_details": {"cached_tokens": 0}, "output_tokens": 187, "output_tokens_details": {"reasoning_tokens": 0}, "total_tokens": 1269}, "user": null, "billing": {"payer": "developer"}, "completed_at": 1770317824, "frequency_penalty": 0.0, "presence_penalty": 0.0, "prompt_cache_retention": null, "store": true}, "reward": 0.0, "uuid": "weather-station-001", "sql": "WITH filtered_logs AS (\n SELECT\n sl.log_id,\n s.station_id,\n ws.station_name,\n sl.drift_date,\n sl.temperature_deviation,\n SUBSTRING_INDEX(sl.reporting_email, '@', -1) AS email_domain\n FROM sensor_log sl\n JOIN sensor s ON sl.sensor_id = s.sensor_id\n JOIN weather_station ws ON s.station_id = ws.station_id\n WHERE (LENGTH(sl.readings) - LENGTH(REPLACE(sl.readings, ',', ''))) >= 2\n),\nagg AS (\n SELECT\n station_id,\n station_name,\n MAX(drift_date) AS latest_drift_date,\n AVG(temperature_deviation) AS avg_temp_deviation\n FROM filtered_logs\n GROUP BY station_id, station_name\n),\nlatest_domain AS (\n SELECT station_id, email_domain\n FROM (\n SELECT\n fl.station_id,\n fl.email_domain,\n ROW_NUMBER() OVER (PARTITION BY fl.station_id ORDER BY fl.drift_date DESC) AS rn\n FROM filtered_logs fl\n ) t\n WHERE rn = 1\n)\nSELECT\n a.station_id,\n a.station_name,\n a.latest_drift_date,\n ROUND(a.avg_temp_deviation, 2) AS avg_temp_deviation,\n d.email_domain\nFROM agg a\nJOIN latest_domain d ON a.station_id = d.station_id\nORDER BY a.avg_temp_deviation DESC;", "model_output": "```sql\nSELECT\n ws.station_id,\n ws.station_name,\n MAX(sl.drift_date) AS latest_drift_date,\n AVG(sl.temperature_deviation) AS avg_temperature_deviation,\n SUBSTRING_INDEX(SUBSTRING_INDEX(sl.reporting_email, '@', -1), '.', -2) AS email_domain\nFROM\n weather_station ws\n JOIN sensor s ON ws.station_id = s.station_id\n JOIN sensor_log sl ON s.sensor_id = sl.sensor_id\nWHERE\n -- Ensure readings array has at least three entries\n (LENGTH(sl.readings) - LENGTH(REPLACE(sl.readings, ',', '')) + 1) >= 3\nGROUP BY\n ws.station_id,\n ws.station_name,\n SUBSTRING_INDEX(SUBSTRING_INDEX(sl.reporting_email, '@', -1), '.', -2)\nORDER BY\n avg_temperature_deviation DESC;\n```", "extracted_sql": "SELECT\n ws.station_id,\n ws.station_name,\n MAX(sl.drift_date) AS latest_drift_date,\n AVG(sl.temperature_deviation) AS avg_temperature_deviation,\n SUBSTRING_INDEX(SUBSTRING_INDEX(sl.reporting_email, '@', -1), '.', -2) AS email_domain\nFROM\n weather_station ws\n JOIN sensor s ON ws.station_id = s.station_id\n JOIN sensor_log sl ON s.sensor_id = sl.sensor_id\nWHERE\n -- Ensure readings array has at least three entries\n (LENGTH(sl.readings) - LENGTH(REPLACE(sl.readings, ',', '')) + 1) >= 3\nGROUP BY\n ws.station_id,\n ws.station_name,\n SUBSTRING_INDEX(SUBSTRING_INDEX(sl.reporting_email, '@', -1), '.', -2)\nORDER BY\n avg_temperature_deviation DESC;", "sql_dialect": "mysql", "sql_context": "CREATE TABLE weather_station (\n station_id INT UNSIGNED AUTO_INCREMENT PRIMARY KEY,\n station_name VARCHAR(100) NOT NULL,\n latitude DECIMAL(9,6) NOT NULL,\n longitude DECIMAL(9,6) NOT NULL,\n installed_date DATE NOT NULL,\n created_at DATETIME NOT NULL,\n updated_at DATETIME NOT NULL,\n is_active TINYINT(1) NOT NULL DEFAULT 1,\n description TEXT\n) ENGINE=InnoDB;\n\nCREATE TABLE sensor (\n sensor_id INT UNSIGNED AUTO_INCREMENT PRIMARY KEY,\n station_id INT UNSIGNED NOT NULL,\n sensor_type VARCHAR(50) NOT NULL,\n calibration_date DATE NOT NULL,\n serial_number VARCHAR(50),\n firmware_version VARCHAR(20),\n is_active TINYINT(1) NOT NULL DEFAULT 1,\n created_at DATETIME NOT NULL,\n updated_at DATETIME NOT NULL,\n description TEXT,\n FOREIGN KEY (station_id) REFERENCES weather_station(station_id)\n) ENGINE=InnoDB;\n\nCREATE TABLE sensor_log (\n log_id BIGINT UNSIGNED AUTO_INCREMENT PRIMARY KEY,\n sensor_id INT UNSIGNED NOT NULL,\n drift_date DATE NOT NULL,\n temperature_deviation DECIMAL(5,2) NOT NULL,\n reporting_email VARCHAR(150) NOT NULL,\n log_url VARCHAR(255) NOT NULL,\n readings TEXT NOT NULL,\n created_at DATETIME NOT NULL,\n updated_at DATETIME NOT NULL,\n is_processed TINYINT(1) NOT NULL DEFAULT 0,\n notes TEXT,\n FOREIGN KEY (sensor_id) REFERENCES sensor(sensor_id)\n) ENGINE=InnoDB;\n\nINSERT INTO weather_station (station_name, latitude, longitude, installed_date, created_at, updated_at, is_active, description) VALUES\n('North Ridge', 45.123456, -122.654321, '2023-03-15', '2023-04-01 08:00:00', '2023-04-01 08:00:00', 1, 'High elevation site'),\n('East Valley', 44.987654, -122.112233, '2022-11-20', '2022-12-01 09:15:00', '2022-12-01 09:15:00', 1, NULL);\n\nINSERT INTO sensor (station_id, sensor_type, calibration_date, serial_number, firmware_version, is_active, created_at, updated_at, description) VALUES\n(1, 'Thermometer', '2025-01-01', 'SN-001', 'v1.2.0', 1, '2025-01-02 08:00:00', '2025-01-02 08:00:00', NULL),\n(2, 'Thermometer', '2024-12-10', 'SN-003', 'v1.1.3', 1, '2024-12-12 09:00:00', '2024-12-12 09:00:00', NULL);\n\nINSERT INTO sensor_log (sensor_id, drift_date, temperature_deviation, reporting_email, log_url, readings, created_at, updated_at, is_processed, notes) VALUES\n(1, '2025-07-01', 0.45, 'tech.north@example.com', 'http://logs.example.com/north/2025-07-01', '[21.2,21.5,21.7]', '2025-07-02 08:10:00', '2025-07-02 08:10:00', 1, NULL),\n(1, '2025-07-15', 0.30, 'ops.north@example.com', 'http://logs.example.com/north/2025-07-15', '[21.0,21.1]', '2025-07-16 08:12:00', '2025-07-16 08:12:00', 0, NULL),\n(2, '2025-07-03', -0.20, 'maintenance@eastvalley.org', 'http://logs.example.com/east/2025-07-03', '[19.8,20.0,20.2,20.3]', '2025-07-04 09:00:00', '2025-07-04 09:00:00', 1, NULL);", "sql_prompt": "Please retrieve for each weather station the latest sensor-drift date, the average temperature deviation, and the domain extracted from the reporting email within the sensor log URL, omitting any records whose embedded readings array contains fewer than three entries, and return the list ordered by descending average deviation.", "judge_passed": false, "failure_reason": "judge_evaluation_failed", "judge_evaluations": [{"responses_create_params": {"background": null, "include": null, "input": [{"content": "You are an expert SQL judge evaluating the functional equivalence of two SQL queries. You will be given SQL query A and SQL query B. Your task is to determine whether both queries would produce identical result sets when executed against the provided database schema.\n\n## Core Evaluation Principle\n\nTwo SQL queries are functionally equivalent if and only if they return the same rows with the same column values for all possible database states conforming to the schema. Focus on semantic equivalence, not syntactic similarity.\n\n## SQL Equivalence Rules\n\n### Always Equivalent (ignore these differences):\n- **Formatting**: Whitespace, line breaks, capitalization of keywords\n- **Aliasing**: Different table/column aliases (e.g., `e.name` vs `employees.name`)\n- **JOIN syntax**: `FROM a, b WHERE a.id = b.id` \u2261 `FROM a JOIN b ON a.id = b.id`\n- **JOIN keyword**: `JOIN` \u2261 `INNER JOIN`\n- **Explicit vs implicit column lists**: When results are identical\n- **Redundant DISTINCT**: When the query already guarantees uniqueness (e.g., selecting a PRIMARY KEY)\n- **Parentheses**: Extra parentheses that don't change evaluation order\n- **Semicolons**: Trailing semicolon presence/absence\n\n### Equivalent Patterns (require careful analysis):\n- **CTE vs Subquery**: `WITH t AS (SELECT ...) SELECT * FROM t` may equal a nested subquery\n- **EXISTS vs IN vs JOIN**: Often equivalent for the same filtering logic\n- **COALESCE vs CASE**: `COALESCE(a, b)` \u2261 `CASE WHEN a IS NOT NULL THEN a ELSE b END`\n- **Boolean expressions**: `NOT (a AND b)` \u2261 `(NOT a) OR (NOT b)` (De Morgan's laws)\n- **Arithmetic**: `price * 1.1` \u2261 `price + price * 0.1`\n- **Date arithmetic**: Dialect-specific but logically equivalent date calculations\n\n### Never Equivalent (these always matter):\n- **Different columns selected**: `SELECT a, b` \u2260 `SELECT a, c`\n- **Different filtering**: `WHERE x > 5` \u2260 `WHERE x >= 5`\n- **Different aggregations**: `SUM(x)` \u2260 `AVG(x)`\n- **Different grouping**: `GROUP BY a` \u2260 `GROUP BY a, b`\n- **Missing/extra DISTINCT**: When it changes the result set\n- **Different ORDER BY**: When ordering is semantically required by the question\n- **Different LIMIT/OFFSET**: When they affect the result set\n- **NULL handling differences**: When they produce different results\n\n## Dialect-Specific Considerations\n\n### MySQL\n- Backtick quoting for identifiers: `table_name`.`column_name`\n- LIMIT syntax: `LIMIT n` or `LIMIT offset, count`\n- String functions: `SUBSTRING_INDEX()`, `CONCAT()`, `GROUP_CONCAT()`\n- Auto-increment: `AUTO_INCREMENT`\n- Boolean: `TINYINT(1)`, `TRUE`/`FALSE`\n\n### PostgreSQL\n- Double-quote identifiers: \"column\"\n- Type casting: `value::type` or `CAST(value AS type)`\n- Array operations: `ANY()`, `ALL()`, array literals\n- String functions: `SPLIT_PART()`, `STRING_AGG()`\n- Sequences: `SERIAL`, `BIGSERIAL`\n- Boolean: Native `BOOLEAN` type\n\n### SQLite\n- Limited type system (TEXT, INTEGER, REAL, BLOB)\n- String concatenation: `||` operator\n- No native BOOLEAN (uses 0/1)\n- `AUTOINCREMENT` keyword\n- No RIGHT JOIN or FULL OUTER JOIN\n\n## Output Format\n\nAnalyze both queries step by step, then provide your verdict:\n- If the queries are functionally equivalent: [[A=B]]\n- If the queries produce different results: [[A!=B]]\n\nExample: \"After analyzing both queries, my verdict is [[A=B]]\".", "role": "system", "type": "message"}, {"content": "<|SQL Dialect|>\nmysql\n\n<|Database Schema and Sample Data|>\nCREATE TABLE weather_station (\n station_id INT UNSIGNED AUTO_INCREMENT PRIMARY KEY,\n station_name VARCHAR(100) NOT NULL,\n latitude DECIMAL(9,6) NOT NULL,\n longitude DECIMAL(9,6) NOT NULL,\n installed_date DATE NOT NULL,\n created_at DATETIME NOT NULL,\n updated_at DATETIME NOT NULL,\n is_active TINYINT(1) NOT NULL DEFAULT 1,\n description TEXT\n) ENGINE=InnoDB;\n\nCREATE TABLE sensor (\n sensor_id INT UNSIGNED AUTO_INCREMENT PRIMARY KEY,\n station_id INT UNSIGNED NOT NULL,\n sensor_type VARCHAR(50) NOT NULL,\n calibration_date DATE NOT NULL,\n serial_number VARCHAR(50),\n firmware_version VARCHAR(20),\n is_active TINYINT(1) NOT NULL DEFAULT 1,\n created_at DATETIME NOT NULL,\n updated_at DATETIME NOT NULL,\n description TEXT,\n FOREIGN KEY (station_id) REFERENCES weather_station(station_id)\n) ENGINE=InnoDB;\n\nCREATE TABLE sensor_log (\n log_id BIGINT UNSIGNED AUTO_INCREMENT PRIMARY KEY,\n sensor_id INT UNSIGNED NOT NULL,\n drift_date DATE NOT NULL,\n temperature_deviation DECIMAL(5,2) NOT NULL,\n reporting_email VARCHAR(150) NOT NULL,\n log_url VARCHAR(255) NOT NULL,\n readings TEXT NOT NULL,\n created_at DATETIME NOT NULL,\n updated_at DATETIME NOT NULL,\n is_processed TINYINT(1) NOT NULL DEFAULT 0,\n notes TEXT,\n FOREIGN KEY (sensor_id) REFERENCES sensor(sensor_id)\n) ENGINE=InnoDB;\n\nINSERT INTO weather_station (station_name, latitude, longitude, installed_date, created_at, updated_at, is_active, description) VALUES\n('North Ridge', 45.123456, -122.654321, '2023-03-15', '2023-04-01 08:00:00', '2023-04-01 08:00:00', 1, 'High elevation site'),\n('East Valley', 44.987654, -122.112233, '2022-11-20', '2022-12-01 09:15:00', '2022-12-01 09:15:00', 1, NULL);\n\nINSERT INTO sensor (station_id, sensor_type, calibration_date, serial_number, firmware_version, is_active, created_at, updated_at, description) VALUES\n(1, 'Thermometer', '2025-01-01', 'SN-001', 'v1.2.0', 1, '2025-01-02 08:00:00', '2025-01-02 08:00:00', NULL),\n(2, 'Thermometer', '2024-12-10', 'SN-003', 'v1.1.3', 1, '2024-12-12 09:00:00', '2024-12-12 09:00:00', NULL);\n\nINSERT INTO sensor_log (sensor_id, drift_date, temperature_deviation, reporting_email, log_url, readings, created_at, updated_at, is_processed, notes) VALUES\n(1, '2025-07-01', 0.45, 'tech.north@example.com', 'http://logs.example.com/north/2025-07-01', '[21.2,21.5,21.7]', '2025-07-02 08:10:00', '2025-07-02 08:10:00', 1, NULL),\n(1, '2025-07-15', 0.30, 'ops.north@example.com', 'http://logs.example.com/north/2025-07-15', '[21.0,21.1]', '2025-07-16 08:12:00', '2025-07-16 08:12:00', 0, NULL),\n(2, '2025-07-03', -0.20, 'maintenance@eastvalley.org', 'http://logs.example.com/east/2025-07-03', '[19.8,20.0,20.2,20.3]', '2025-07-04 09:00:00', '2025-07-04 09:00:00', 1, NULL);\n\n<|Natural Language Question|>\nPlease retrieve for each weather station the latest sensor-drift date, the average temperature deviation, and the domain extracted from the reporting email within the sensor log URL, omitting any records whose embedded readings array contains fewer than three entries, and return the list ordered by descending average deviation.\n\n<|Start of SQL Query A|>\nWITH filtered_logs AS (\n SELECT\n sl.log_id,\n s.station_id,\n ws.station_name,\n sl.drift_date,\n sl.temperature_deviation,\n SUBSTRING_INDEX(sl.reporting_email, '@', -1) AS email_domain\n FROM sensor_log sl\n JOIN sensor s ON sl.sensor_id = s.sensor_id\n JOIN weather_station ws ON s.station_id = ws.station_id\n WHERE (LENGTH(sl.readings) - LENGTH(REPLACE(sl.readings, ',', ''))) >= 2\n),\nagg AS (\n SELECT\n station_id,\n station_name,\n MAX(drift_date) AS latest_drift_date,\n AVG(temperature_deviation) AS avg_temp_deviation\n FROM filtered_logs\n GROUP BY station_id, station_name\n),\nlatest_domain AS (\n SELECT station_id, email_domain\n FROM (\n SELECT\n fl.station_id,\n fl.email_domain,\n ROW_NUMBER() OVER (PARTITION BY fl.station_id ORDER BY fl.drift_date DESC) AS rn\n FROM filtered_logs fl\n ) t\n WHERE rn = 1\n)\nSELECT\n a.station_id,\n a.station_name,\n a.latest_drift_date,\n ROUND(a.avg_temp_deviation, 2) AS avg_temp_deviation,\n d.email_domain\nFROM agg a\nJOIN latest_domain d ON a.station_id = d.station_id\nORDER BY a.avg_temp_deviation DESC;\n<|End of SQL Query A|>\n\n<|Start of SQL Query B|>\nSELECT\n ws.station_id,\n ws.station_name,\n MAX(sl.drift_date) AS latest_drift_date,\n AVG(sl.temperature_deviation) AS avg_temperature_deviation,\n SUBSTRING_INDEX(SUBSTRING_INDEX(sl.reporting_email, '@', -1), '.', -2) AS email_domain\nFROM\n weather_station ws\n JOIN sensor s ON ws.station_id = s.station_id\n JOIN sensor_log sl ON s.sensor_id = sl.sensor_id\nWHERE\n -- Ensure readings array has at least three entries\n (LENGTH(sl.readings) - LENGTH(REPLACE(sl.readings, ',', '')) + 1) >= 3\nGROUP BY\n ws.station_id,\n ws.station_name,\n SUBSTRING_INDEX(SUBSTRING_INDEX(sl.reporting_email, '@', -1), '.', -2)\nORDER BY\n avg_temperature_deviation DESC;\n<|End of SQL Query B|>", "role": "user", "type": "message"}], "instructions": null, "max_output_tokens": null, "max_tool_calls": null, "metadata": null, "model": null, "parallel_tool_calls": true, "previous_response_id": null, "prompt": null, "reasoning": null, "service_tier": null, "store": null, "temperature": null, "text": null, "tool_choice": "auto", "tools": [], "top_logprobs": null, "top_p": null, "truncation": null, "user": null, "stream": null}, "response": {"id": "resp_070d35fe73e926db006984e800a3188190b51e445e354b3c13", "created_at": 1770317824.0, "error": null, "incomplete_details": null, "instructions": null, "metadata": {}, "model": "gpt-4.1-2025-04-14", "object": "response", "output": [{"id": "msg_070d35fe73e926db006984e8014ee881908007f957be6177f3", "content": [{"annotations": [], "text": "Let's analyze both queries step by step in the context of the schema and sample data.\n\n---\n\n## Step 1: **Filtering by readings array count**\n\n**Query A:**\n```sql\nWHERE (LENGTH(sl.readings) - LENGTH(REPLACE(sl.readings, ',', ''))) >= 2\n```\n- This counts the number of commas in the readings string. If there are at least **2 commas**, then the array has at least 3 entries:\n - readings = `[21.2,21.5,21.7]` \u21d2 2 commas \u21d2 SELECTED\n - readings = `[21.0,21.1]` \u21d2 1 comma \u21d2 NOT selected\n\n**Query B:**\n```sql\nWHERE (LENGTH(sl.readings) - LENGTH(REPLACE(sl.readings, ',', '')) + 1) >= 3\n```\n- Here, the number of entries is calculated directly as commas + 1. So, at least 3 entries, or at least 2 commas.\n- Both queries are equivalent in this aspect: they filter for readings arrays with three or more entries.\n\n**Verdict**: Filtering is equivalent.\n\n---\n\n## Step 2: **Grouping and Aggregation**\n\n### Query A\n\n- **CTE `filtered_logs`**: Prepares the set of filtered sensor logs, extracts `station_id`, `station_name`, `drift_date`, `temperature_deviation`, and the domain from the reporting email (`SUBSTRING_INDEX(sl.reporting_email, '@', -1)`).\n- **CTE `agg`**: Aggregates by station, computing:\n - `MAX(drift_date)` (latest drift date for station)\n - `AVG(temperature_deviation)`\n- **CTE `latest_domain`**: For each station, picks the reporting email domain from the *latest* drift date log.\n - Uses ROW_NUMBER() OVER (PARTITION BY station_id ORDER BY drift_date DESC)\n- **Final SELECT**: Joins `agg` and `latest_domain` by `station_id`. Output is:\n - station_id, station_name, latest_drift_date, avg_temp_deviation (rounded), latest log's email domain\n - Ordered by avg_temp_deviation DESC\n\n### Query B\n\n- Performs a **single GROUP BY** over:\n - `ws.station_id, ws.station_name, SUBSTRING_INDEX(SUBSTRING_INDEX(sl.reporting_email, '@', -1), '.', -2)`\n- **Aggregates**:\n - `MAX(sl.drift_date) as latest_drift_date`\n - `AVG(sl.temperature_deviation)`\n - `SUBSTRING_INDEX(SUBSTRING_INDEX(sl.reporting_email, '@', -1), '.', -2) as email_domain`\n\n**Key difference**:\n\n- Query A: Groups and computes aggregations at the station level, then finds the reporting email domain specifically from the most recent (latest) drift date log for each station.\n- Query B: **GROUP BY** is at `(station_id, station_name, derived email_domain)`. This means:\n - If a station has logs with different email domains (which is possible if reporting_email varies across logs), Query B will produce *multiple* rows per station for each different domain in filtered logs.\n - The reported `latest_drift_date` and `AVG(temperature_deviation)` are per (station, email_domain) group, **not per station**.\n\n**Example with sample data:**\n\nWith given data and filtering, for North Ridge (station_id=1):\n- Only log_id=1 (reading with 3 values) passes the filter: reporting_email = 'tech.north@example.com' \u2192 domain = 'example.com'\n- For East Valley: only log_id=3 passes.\n\nSo, with this data, both queries would output 1 row per station.\n\n**But**: if a station has valid logs with different email domains, Query A would only **display one row per station** (with the email domain of the latest log), while Query B would emit **one row per domain per station**.\n\n---\n\n## Step 3: **Email domain extraction**\n\n- **Query A** uses: `SUBSTRING_INDEX(sl.reporting_email, '@', -1)` \u21d2 everything after last \"@\"\n- **Query B** uses: `SUBSTRING_INDEX(SUBSTRING_INDEX(sl.reporting_email, '@', -1), '.', -2)`\n - This further extracts the last two parts (e.g. \"sub.domain.com\" \u2192 \"domain.com\")\n- **Sample data**: All emails use \"example.com\" or \"eastvalley.org\", so extraction would match.\n- **In general**: If the domain is \"sub.example.com\", Query A returns \"sub.example.com\", Query B returns \"example.com\".\n\n**Verdict**: **Not equivalent** if more complex domains exist.\n\n---\n\n## Step 4: **Ordering**\n\nBoth order by average deviation descending.\n\n---\n\n## Step 5: **Summary**\n\n### **Differences**\n1. **Grouping**: Query B may return multiple rows per station if reporting emails have multiple domains; Query A always returns one row per station.\n2. **Which domain**: Query A always uses the domain from the latest log; Query B groups by domains, and may not show the domain of the latest log unless it coincides with the group that is the latest. If there are multiple email domains, Query B can output more rows.\n3. **Domain extraction logic**: Query A gets the full domain after \"@\", Query B tries to restrict to the last two components of the domain, which might be different for e.g. 'user@mail.sub.domain.com'.\n\n**On certain data sets (like your sample), results might look similar, but for general schemas, results differ.**\n\n---\n\n# Verdict\n\nAfter analyzing both queries, my verdict is **[[A!=B]]**:\n- Query A guarantees one row per station (using the *latest* log's email domain).\n- Query B can return multiple rows per station if multiple domains appear, aggregates by (station, domain), and its domain extraction is different.", "type": "output_text", "logprobs": []}], "role": "assistant", "status": "completed", "type": "message"}], "parallel_tool_calls": true, "temperature": 1.0, "tool_choice": "auto", "tools": [], "top_p": 1.0, "background": false, "conversation": null, "max_output_tokens": null, "max_tool_calls": null, "previous_response_id": null, "prompt": null, "prompt_cache_key": null, "reasoning": {"effort": null, "generate_summary": null, "summary": null}, "safety_identifier": null, "service_tier": "default", "status": "completed", "text": {"format": {"type": "text"}, "verbosity": "medium"}, "top_logprobs": 0, "truncation": "disabled", "usage": {"input_tokens": 2392, "input_tokens_details": {"cached_tokens": 0}, "output_tokens": 1253, "output_tokens_details": {"reasoning_tokens": 0}, "total_tokens": 3645}, "user": null, "billing": {"payer": "developer"}, "completed_at": 1770317852, "frequency_penalty": 0.0, "presence_penalty": 0.0, "prompt_cache_retention": null, "store": true}, "verdict_label": "[[A!=B]]"}], "metadata": null} -{"responses_create_params": {"background": null, "include": null, "input": [{"content": "You are a SQL assistant who expertly maps natural language commands and problems into clean, efficient, and well-structured SQL queries. You always carefully examine the database context to include only the relevant tables and fields. You return only SQL code (no explanations outside of SQL code block; only proper comments are permitted). Wrap your SQL query inside a ```sql``` code block.", "role": "system", "type": "message"}, {"content": "DIALECT: postgresql\n\nDATABASE CONTEXT:\nCREATE TABLE users (\n user_id SERIAL PRIMARY KEY,\n username VARCHAR(50) UNIQUE NOT NULL,\n email VARCHAR(150) NOT NULL,\n registration_date DATE NOT NULL,\n is_premium BOOLEAN DEFAULT FALSE\n);\n\nCREATE TABLE posts (\n post_id SERIAL PRIMARY KEY,\n user_id INT REFERENCES users(user_id),\n title VARCHAR(200) NOT NULL,\n content TEXT,\n created_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP,\n view_count INT DEFAULT 0\n);\n\nCREATE TABLE comments (\n comment_id SERIAL PRIMARY KEY,\n post_id INT REFERENCES posts(post_id),\n user_id INT REFERENCES users(user_id),\n comment_text TEXT NOT NULL,\n created_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP\n);\n\nINSERT INTO users (username, email, registration_date, is_premium) VALUES\n('alice_dev', 'alice@example.com', '2024-01-15', TRUE),\n('bob_writer', 'bob@example.com', '2024-02-20', FALSE),\n('carol_blogger', 'carol@example.com', '2024-03-10', TRUE);\n\nINSERT INTO posts (user_id, title, content, view_count) VALUES\n(1, 'Getting Started with PostgreSQL', 'A beginner guide...', 1500),\n(1, 'Advanced SQL Techniques', 'Deep dive into...', 2300),\n(1, 'Database Optimization', 'Tips for performance...', 1800),\n(1, 'SQL Best Practices', 'Industry standards...', 900),\n(1, 'Query Tuning Guide', 'How to optimize...', 1200),\n(2, 'Writing Tips', 'How to write better...', 500),\n(3, 'My First Blog Post', 'Hello world!', 100);\n\nINSERT INTO comments (post_id, user_id, comment_text) VALUES\n(1, 2, 'Great introduction!'),\n(1, 3, 'Very helpful, thanks!'),\n(2, 2, 'Learned a lot from this'),\n(2, 3, 'Can you elaborate on CTEs?'),\n(3, 2, 'Nice tips!'),\n(4, 3, 'Following these now'),\n(5, 2, 'Bookmarked for later');\n\nQUESTION:\nFind the average number of comments per post for each user, but only include users who have created at least 5 posts. Show username, total posts, total comments received, and average comments per post.", "role": "user", "type": "message"}], "instructions": null, "max_output_tokens": null, "max_tool_calls": null, "metadata": null, "model": null, "parallel_tool_calls": true, "previous_response_id": null, "prompt": null, "reasoning": null, "service_tier": null, "store": null, "temperature": null, "text": null, "tool_choice": "auto", "tools": [], "top_logprobs": null, "top_p": null, "truncation": null, "user": null, "stream": null}, "response": {"id": "resp_0a406ea41bb7585d006984e7fc7aec8195b9dbe81a2b038c91", "created_at": 1770317820.0, "error": null, "incomplete_details": null, "instructions": null, "metadata": {}, "model": "gpt-4.1-2025-04-14", "object": "response", "output": [{"id": "msg_0a406ea41bb7585d006984e7fd06fc8195b8ef772bbf4fda40", "content": [{"annotations": [], "text": "```sql\nSELECT\n u.username,\n COUNT(p.post_id) AS total_posts,\n COALESCE(SUM(pc.comment_count), 0) AS total_comments_received,\n ROUND(COALESCE(SUM(pc.comment_count), 0)::numeric / COUNT(p.post_id), 2) AS avg_comments_per_post\nFROM\n users u\nJOIN\n posts p ON u.user_id = p.user_id\nLEFT JOIN (\n SELECT\n post_id,\n COUNT(comment_id) AS comment_count\n FROM\n comments\n GROUP BY\n post_id\n) pc ON p.post_id = pc.post_id\nGROUP BY\n u.user_id, u.username\nHAVING\n COUNT(p.post_id) >= 5\nORDER BY\n avg_comments_per_post DESC,\n u.username;\n```", "type": "output_text", "logprobs": []}], "role": "assistant", "status": "completed", "type": "message"}], "parallel_tool_calls": true, "temperature": 1.0, "tool_choice": "auto", "tools": [], "top_p": 1.0, "background": false, "conversation": null, "max_output_tokens": null, "max_tool_calls": null, "previous_response_id": null, "prompt": null, "prompt_cache_key": null, "reasoning": {"effort": null, "generate_summary": null, "summary": null}, "safety_identifier": null, "service_tier": "default", "status": "completed", "text": {"format": {"type": "text"}, "verbosity": "medium"}, "top_logprobs": 0, "truncation": "disabled", "usage": {"input_tokens": 594, "input_tokens_details": {"cached_tokens": 0}, "output_tokens": 172, "output_tokens_details": {"reasoning_tokens": 0}, "total_tokens": 766}, "user": null, "billing": {"payer": "developer"}, "completed_at": 1770317822, "frequency_penalty": 0.0, "presence_penalty": 0.0, "prompt_cache_retention": null, "store": true}, "reward": 1.0, "uuid": "social-media-001", "sql": "SELECT\n u.username,\n COUNT(DISTINCT p.post_id) AS total_posts,\n COUNT(c.comment_id) AS total_comments,\n ROUND(COUNT(c.comment_id)::NUMERIC / COUNT(DISTINCT p.post_id), 2) AS avg_comments_per_post\nFROM users u\nJOIN posts p ON u.user_id = p.user_id\nLEFT JOIN comments c ON p.post_id = c.post_id\nGROUP BY u.user_id, u.username\nHAVING COUNT(DISTINCT p.post_id) >= 5\nORDER BY avg_comments_per_post DESC;", "model_output": "```sql\nSELECT\n u.username,\n COUNT(p.post_id) AS total_posts,\n COALESCE(SUM(pc.comment_count), 0) AS total_comments_received,\n ROUND(COALESCE(SUM(pc.comment_count), 0)::numeric / COUNT(p.post_id), 2) AS avg_comments_per_post\nFROM\n users u\nJOIN\n posts p ON u.user_id = p.user_id\nLEFT JOIN (\n SELECT\n post_id,\n COUNT(comment_id) AS comment_count\n FROM\n comments\n GROUP BY\n post_id\n) pc ON p.post_id = pc.post_id\nGROUP BY\n u.user_id, u.username\nHAVING\n COUNT(p.post_id) >= 5\nORDER BY\n avg_comments_per_post DESC,\n u.username;\n```", "extracted_sql": "SELECT\n u.username,\n COUNT(p.post_id) AS total_posts,\n COALESCE(SUM(pc.comment_count), 0) AS total_comments_received,\n ROUND(COALESCE(SUM(pc.comment_count), 0)::numeric / COUNT(p.post_id), 2) AS avg_comments_per_post\nFROM\n users u\nJOIN\n posts p ON u.user_id = p.user_id\nLEFT JOIN (\n SELECT\n post_id,\n COUNT(comment_id) AS comment_count\n FROM\n comments\n GROUP BY\n post_id\n) pc ON p.post_id = pc.post_id\nGROUP BY\n u.user_id, u.username\nHAVING\n COUNT(p.post_id) >= 5\nORDER BY\n avg_comments_per_post DESC,\n u.username;", "sql_dialect": "postgresql", "sql_context": "CREATE TABLE users (\n user_id SERIAL PRIMARY KEY,\n username VARCHAR(50) UNIQUE NOT NULL,\n email VARCHAR(150) NOT NULL,\n registration_date DATE NOT NULL,\n is_premium BOOLEAN DEFAULT FALSE\n);\n\nCREATE TABLE posts (\n post_id SERIAL PRIMARY KEY,\n user_id INT REFERENCES users(user_id),\n title VARCHAR(200) NOT NULL,\n content TEXT,\n created_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP,\n view_count INT DEFAULT 0\n);\n\nCREATE TABLE comments (\n comment_id SERIAL PRIMARY KEY,\n post_id INT REFERENCES posts(post_id),\n user_id INT REFERENCES users(user_id),\n comment_text TEXT NOT NULL,\n created_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP\n);\n\nINSERT INTO users (username, email, registration_date, is_premium) VALUES\n('alice_dev', 'alice@example.com', '2024-01-15', TRUE),\n('bob_writer', 'bob@example.com', '2024-02-20', FALSE),\n('carol_blogger', 'carol@example.com', '2024-03-10', TRUE);\n\nINSERT INTO posts (user_id, title, content, view_count) VALUES\n(1, 'Getting Started with PostgreSQL', 'A beginner guide...', 1500),\n(1, 'Advanced SQL Techniques', 'Deep dive into...', 2300),\n(1, 'Database Optimization', 'Tips for performance...', 1800),\n(1, 'SQL Best Practices', 'Industry standards...', 900),\n(1, 'Query Tuning Guide', 'How to optimize...', 1200),\n(2, 'Writing Tips', 'How to write better...', 500),\n(3, 'My First Blog Post', 'Hello world!', 100);\n\nINSERT INTO comments (post_id, user_id, comment_text) VALUES\n(1, 2, 'Great introduction!'),\n(1, 3, 'Very helpful, thanks!'),\n(2, 2, 'Learned a lot from this'),\n(2, 3, 'Can you elaborate on CTEs?'),\n(3, 2, 'Nice tips!'),\n(4, 3, 'Following these now'),\n(5, 2, 'Bookmarked for later');", "sql_prompt": "Find the average number of comments per post for each user, but only include users who have created at least 5 posts. Show username, total posts, total comments received, and average comments per post.", "judge_passed": true, "failure_reason": "none", "judge_evaluations": [{"responses_create_params": {"background": null, "include": null, "input": [{"content": "You are an expert SQL judge evaluating the functional equivalence of two SQL queries. You will be given SQL query A and SQL query B. Your task is to determine whether both queries would produce identical result sets when executed against the provided database schema.\n\n## Core Evaluation Principle\n\nTwo SQL queries are functionally equivalent if and only if they return the same rows with the same column values for all possible database states conforming to the schema. Focus on semantic equivalence, not syntactic similarity.\n\n## SQL Equivalence Rules\n\n### Always Equivalent (ignore these differences):\n- **Formatting**: Whitespace, line breaks, capitalization of keywords\n- **Aliasing**: Different table/column aliases (e.g., `e.name` vs `employees.name`)\n- **JOIN syntax**: `FROM a, b WHERE a.id = b.id` \u2261 `FROM a JOIN b ON a.id = b.id`\n- **JOIN keyword**: `JOIN` \u2261 `INNER JOIN`\n- **Explicit vs implicit column lists**: When results are identical\n- **Redundant DISTINCT**: When the query already guarantees uniqueness (e.g., selecting a PRIMARY KEY)\n- **Parentheses**: Extra parentheses that don't change evaluation order\n- **Semicolons**: Trailing semicolon presence/absence\n\n### Equivalent Patterns (require careful analysis):\n- **CTE vs Subquery**: `WITH t AS (SELECT ...) SELECT * FROM t` may equal a nested subquery\n- **EXISTS vs IN vs JOIN**: Often equivalent for the same filtering logic\n- **COALESCE vs CASE**: `COALESCE(a, b)` \u2261 `CASE WHEN a IS NOT NULL THEN a ELSE b END`\n- **Boolean expressions**: `NOT (a AND b)` \u2261 `(NOT a) OR (NOT b)` (De Morgan's laws)\n- **Arithmetic**: `price * 1.1` \u2261 `price + price * 0.1`\n- **Date arithmetic**: Dialect-specific but logically equivalent date calculations\n\n### Never Equivalent (these always matter):\n- **Different columns selected**: `SELECT a, b` \u2260 `SELECT a, c`\n- **Different filtering**: `WHERE x > 5` \u2260 `WHERE x >= 5`\n- **Different aggregations**: `SUM(x)` \u2260 `AVG(x)`\n- **Different grouping**: `GROUP BY a` \u2260 `GROUP BY a, b`\n- **Missing/extra DISTINCT**: When it changes the result set\n- **Different ORDER BY**: When ordering is semantically required by the question\n- **Different LIMIT/OFFSET**: When they affect the result set\n- **NULL handling differences**: When they produce different results\n\n## Dialect-Specific Considerations\n\n### MySQL\n- Backtick quoting for identifiers: `table_name`.`column_name`\n- LIMIT syntax: `LIMIT n` or `LIMIT offset, count`\n- String functions: `SUBSTRING_INDEX()`, `CONCAT()`, `GROUP_CONCAT()`\n- Auto-increment: `AUTO_INCREMENT`\n- Boolean: `TINYINT(1)`, `TRUE`/`FALSE`\n\n### PostgreSQL\n- Double-quote identifiers: \"column\"\n- Type casting: `value::type` or `CAST(value AS type)`\n- Array operations: `ANY()`, `ALL()`, array literals\n- String functions: `SPLIT_PART()`, `STRING_AGG()`\n- Sequences: `SERIAL`, `BIGSERIAL`\n- Boolean: Native `BOOLEAN` type\n\n### SQLite\n- Limited type system (TEXT, INTEGER, REAL, BLOB)\n- String concatenation: `||` operator\n- No native BOOLEAN (uses 0/1)\n- `AUTOINCREMENT` keyword\n- No RIGHT JOIN or FULL OUTER JOIN\n\n## Output Format\n\nAnalyze both queries step by step, then provide your verdict:\n- If the queries are functionally equivalent: [[A=B]]\n- If the queries produce different results: [[A!=B]]\n\nExample: \"After analyzing both queries, my verdict is [[A=B]]\".", "role": "system", "type": "message"}, {"content": "<|SQL Dialect|>\npostgresql\n\n<|Database Schema and Sample Data|>\nCREATE TABLE users (\n user_id SERIAL PRIMARY KEY,\n username VARCHAR(50) UNIQUE NOT NULL,\n email VARCHAR(150) NOT NULL,\n registration_date DATE NOT NULL,\n is_premium BOOLEAN DEFAULT FALSE\n);\n\nCREATE TABLE posts (\n post_id SERIAL PRIMARY KEY,\n user_id INT REFERENCES users(user_id),\n title VARCHAR(200) NOT NULL,\n content TEXT,\n created_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP,\n view_count INT DEFAULT 0\n);\n\nCREATE TABLE comments (\n comment_id SERIAL PRIMARY KEY,\n post_id INT REFERENCES posts(post_id),\n user_id INT REFERENCES users(user_id),\n comment_text TEXT NOT NULL,\n created_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP\n);\n\nINSERT INTO users (username, email, registration_date, is_premium) VALUES\n('alice_dev', 'alice@example.com', '2024-01-15', TRUE),\n('bob_writer', 'bob@example.com', '2024-02-20', FALSE),\n('carol_blogger', 'carol@example.com', '2024-03-10', TRUE);\n\nINSERT INTO posts (user_id, title, content, view_count) VALUES\n(1, 'Getting Started with PostgreSQL', 'A beginner guide...', 1500),\n(1, 'Advanced SQL Techniques', 'Deep dive into...', 2300),\n(1, 'Database Optimization', 'Tips for performance...', 1800),\n(1, 'SQL Best Practices', 'Industry standards...', 900),\n(1, 'Query Tuning Guide', 'How to optimize...', 1200),\n(2, 'Writing Tips', 'How to write better...', 500),\n(3, 'My First Blog Post', 'Hello world!', 100);\n\nINSERT INTO comments (post_id, user_id, comment_text) VALUES\n(1, 2, 'Great introduction!'),\n(1, 3, 'Very helpful, thanks!'),\n(2, 2, 'Learned a lot from this'),\n(2, 3, 'Can you elaborate on CTEs?'),\n(3, 2, 'Nice tips!'),\n(4, 3, 'Following these now'),\n(5, 2, 'Bookmarked for later');\n\n<|Natural Language Question|>\nFind the average number of comments per post for each user, but only include users who have created at least 5 posts. Show username, total posts, total comments received, and average comments per post.\n\n<|Start of SQL Query A|>\nSELECT\n u.username,\n COUNT(DISTINCT p.post_id) AS total_posts,\n COUNT(c.comment_id) AS total_comments,\n ROUND(COUNT(c.comment_id)::NUMERIC / COUNT(DISTINCT p.post_id), 2) AS avg_comments_per_post\nFROM users u\nJOIN posts p ON u.user_id = p.user_id\nLEFT JOIN comments c ON p.post_id = c.post_id\nGROUP BY u.user_id, u.username\nHAVING COUNT(DISTINCT p.post_id) >= 5\nORDER BY avg_comments_per_post DESC;\n<|End of SQL Query A|>\n\n<|Start of SQL Query B|>\nSELECT\n u.username,\n COUNT(p.post_id) AS total_posts,\n COALESCE(SUM(pc.comment_count), 0) AS total_comments_received,\n ROUND(COALESCE(SUM(pc.comment_count), 0)::numeric / COUNT(p.post_id), 2) AS avg_comments_per_post\nFROM\n users u\nJOIN\n posts p ON u.user_id = p.user_id\nLEFT JOIN (\n SELECT\n post_id,\n COUNT(comment_id) AS comment_count\n FROM\n comments\n GROUP BY\n post_id\n) pc ON p.post_id = pc.post_id\nGROUP BY\n u.user_id, u.username\nHAVING\n COUNT(p.post_id) >= 5\nORDER BY\n avg_comments_per_post DESC,\n u.username;\n<|End of SQL Query B|>", "role": "user", "type": "message"}], "instructions": null, "max_output_tokens": null, "max_tool_calls": null, "metadata": null, "model": null, "parallel_tool_calls": true, "previous_response_id": null, "prompt": null, "reasoning": null, "service_tier": null, "store": null, "temperature": null, "text": null, "tool_choice": "auto", "tools": [], "top_logprobs": null, "top_p": null, "truncation": null, "user": null, "stream": null}, "response": {"id": "resp_0dd4a1645b1210e7006984e7ff31ac8194898985618170aca5", "created_at": 1770317823.0, "error": null, "incomplete_details": null, "instructions": null, "metadata": {}, "model": "gpt-4.1-2025-04-14", "object": "response", "output": [{"id": "msg_0dd4a1645b1210e7006984e7ffaf548194b853468889340d33", "content": [{"annotations": [], "text": "Let's analyze both queries step by step against the **question** and **schema**:\n\n---\n\n### **Natural Language Question**\n\n> Find the average number of comments per post for each user, but only include users who have created at least 5 posts. Show username, total posts, total comments received, and average comments per post.\n\n---\n\n## **Query A Analysis**\n\n```sql\nSELECT\n u.username,\n COUNT(DISTINCT p.post_id) AS total_posts,\n COUNT(c.comment_id) AS total_comments,\n ROUND(COUNT(c.comment_id)::NUMERIC / COUNT(DISTINCT p.post_id), 2) AS avg_comments_per_post\nFROM users u\nJOIN posts p ON u.user_id = p.user_id\nLEFT JOIN comments c ON p.post_id = c.post_id\nGROUP BY u.user_id, u.username\nHAVING COUNT(DISTINCT p.post_id) >= 5\nORDER BY avg_comments_per_post DESC;\n```\n\n- **Joins**: Users to their posts, posts to their comments (LEFT JOIN to include posts with 0 comments).\n- **COUNT(DISTINCT p.post_id)**: Counts unique posts per user (important if somehow posts are duplicated by joins).\n- **COUNT(c.comment_id)**: Counts total comments received across all posts of the user.\n- **Average**: Divides total comments by distinct posts, rounded to 2 decimals.\n- **HAVING**: Filters for users with at least 5 posts.\n- **ORDER**: By average comments per post descending.\n\n---\n\n## **Query B Analysis**\n\n```sql\nSELECT\n u.username,\n COUNT(p.post_id) AS total_posts,\n COALESCE(SUM(pc.comment_count), 0) AS total_comments_received,\n ROUND(COALESCE(SUM(pc.comment_count), 0)::numeric / COUNT(p.post_id), 2) AS avg_comments_per_post\nFROM\n users u\nJOIN\n posts p ON u.user_id = p.user_id\nLEFT JOIN (\n SELECT\n post_id,\n COUNT(comment_id) AS comment_count\n FROM\n comments\n GROUP BY\n post_id\n) pc ON p.post_id = pc.post_id\nGROUP BY\n u.user_id, u.username\nHAVING\n COUNT(p.post_id) >= 5\nORDER BY\n avg_comments_per_post DESC,\n u.username;\n```\n\n- **Joins**: Users to posts, posts to a derived table (`pc`) with number of comments per post.\n- **COUNT(p.post_id)**: Counts posts per user (not DISTINCT, but posts are unique due to PK).\n- **SUM(pc.comment_count)**: For each post, sums its comment count (0 if no comments).\n - COALESCE ensures nulls become 0 (posts with no comments).\n- **Average**: Same as above logic.\n- **HAVING**: Users with at least 5 posts.\n- **ORDER**: Same as Query A, but secondary by username.\n\n---\n\n## **Step-by-step Comparison/Evaluation**\n\n1. **Joins and Aggregation Key**:\n - Both queries join the same tables, grouping by user.\n - Both produce one row per user.\n\n2. **Counting Posts:**\n - Query A: `COUNT(DISTINCT p.post_id)`\n - Query B: `COUNT(p.post_id)`\n - Since `p.post_id` is PK for posts and joined directly, not duplicated by the LEFT JOIN in either, both counts return same value per user.\n\n3. **Counting Comments:**\n - Query A: `COUNT(c.comment_id)`\u2014counts all comment rows per user's post (many NULLs if some posts have no comments, which is handled).\n - Query B: Preaggregates comment counts per post, then sums them, which also gives total comments across all posts. Uses COALESCE to turn NULL to 0 (so posts with no comments are counted as zero).\n\n - **Are these counts always equal?**\n - Yes. For each post, either both count all comments, or zero if none.\n - Query A's LEFT JOIN on comments expands each post into multiple rows (per comment), and COUNT(c.comment_id) skips NULLs (so posts with no comments don't add to the count).\n - Query B's derived table counts comments per post, sums over posts.\n\n - **No double-counting nor missing comments.** These are equivalent aggregations.\n\n4. **Average Calculation:**\n - Both: `ROUND(total_comments / total_posts, 2)`\n - No difference.\n\n5. **Filtering (HAVING):**\n - Both: Require users with 5+ posts.\n\n6. **Columns Output:**\n - Query A: `username`, `total_posts`, `total_comments`, `avg_comments_per_post`\n - Query B: `username`, `total_posts`, `total_comments_received`, `avg_comments_per_post`\n - The only difference is the alias name `total_comments` vs `total_comments_received`. For the result set (column order and data), this is irrelevant.\n\n7. **Ordering**\n - Both: `ORDER BY avg_comments_per_post DESC`\n - Query B additionally orders ties by `username`, introducing deterministic ordering for ties where Query A leaves result indeterminate. Unless the \"required output order\" is specified in the question, this is not relevant for equivalence (results still have same rows with same per-column values).\n\n---\n\n## **Corner Cases Consideration**\n\n- **Users with No Posts**: Both do not include such users due to the `JOIN posts` and the HAVING clause.\n- **Posts with No Comments**: Both assign zero comments for such posts properly.\n- **Multiple Comments per Post**: Both count all comments correctly.\n\n---\n\n## **Verdict**\n\nThere are no semantic differences in the result sets (row content and calculations) produced by the two queries\u2014both produce one row per user with at least five posts, outputting username, total posts, total comments received (or just total comments), and average number of comments per post.\n\nAfter analyzing both queries, my verdict is **[[A=B]]**.", "type": "output_text", "logprobs": []}], "role": "assistant", "status": "completed", "type": "message"}], "parallel_tool_calls": true, "temperature": 1.0, "tool_choice": "auto", "tools": [], "top_p": 1.0, "background": false, "conversation": null, "max_output_tokens": null, "max_tool_calls": null, "previous_response_id": null, "prompt": null, "prompt_cache_key": null, "reasoning": {"effort": null, "generate_summary": null, "summary": null}, "safety_identifier": null, "service_tier": "default", "status": "completed", "text": {"format": {"type": "text"}, "verbosity": "medium"}, "top_logprobs": 0, "truncation": "disabled", "usage": {"input_tokens": 1707, "input_tokens_details": {"cached_tokens": 0}, "output_tokens": 1270, "output_tokens_details": {"reasoning_tokens": 0}, "total_tokens": 2977}, "user": null, "billing": {"payer": "developer"}, "completed_at": 1770317848, "frequency_penalty": 0.0, "presence_penalty": 0.0, "prompt_cache_retention": null, "store": true}, "verdict_label": "[[A=B]]"}, {"responses_create_params": {"background": null, "include": null, "input": [{"content": "You are an expert SQL judge evaluating the functional equivalence of two SQL queries. You will be given SQL query A and SQL query B. Your task is to determine whether both queries would produce identical result sets when executed against the provided database schema.\n\n## Core Evaluation Principle\n\nTwo SQL queries are functionally equivalent if and only if they return the same rows with the same column values for all possible database states conforming to the schema. Focus on semantic equivalence, not syntactic similarity.\n\n## SQL Equivalence Rules\n\n### Always Equivalent (ignore these differences):\n- **Formatting**: Whitespace, line breaks, capitalization of keywords\n- **Aliasing**: Different table/column aliases (e.g., `e.name` vs `employees.name`)\n- **JOIN syntax**: `FROM a, b WHERE a.id = b.id` \u2261 `FROM a JOIN b ON a.id = b.id`\n- **JOIN keyword**: `JOIN` \u2261 `INNER JOIN`\n- **Explicit vs implicit column lists**: When results are identical\n- **Redundant DISTINCT**: When the query already guarantees uniqueness (e.g., selecting a PRIMARY KEY)\n- **Parentheses**: Extra parentheses that don't change evaluation order\n- **Semicolons**: Trailing semicolon presence/absence\n\n### Equivalent Patterns (require careful analysis):\n- **CTE vs Subquery**: `WITH t AS (SELECT ...) SELECT * FROM t` may equal a nested subquery\n- **EXISTS vs IN vs JOIN**: Often equivalent for the same filtering logic\n- **COALESCE vs CASE**: `COALESCE(a, b)` \u2261 `CASE WHEN a IS NOT NULL THEN a ELSE b END`\n- **Boolean expressions**: `NOT (a AND b)` \u2261 `(NOT a) OR (NOT b)` (De Morgan's laws)\n- **Arithmetic**: `price * 1.1` \u2261 `price + price * 0.1`\n- **Date arithmetic**: Dialect-specific but logically equivalent date calculations\n\n### Never Equivalent (these always matter):\n- **Different columns selected**: `SELECT a, b` \u2260 `SELECT a, c`\n- **Different filtering**: `WHERE x > 5` \u2260 `WHERE x >= 5`\n- **Different aggregations**: `SUM(x)` \u2260 `AVG(x)`\n- **Different grouping**: `GROUP BY a` \u2260 `GROUP BY a, b`\n- **Missing/extra DISTINCT**: When it changes the result set\n- **Different ORDER BY**: When ordering is semantically required by the question\n- **Different LIMIT/OFFSET**: When they affect the result set\n- **NULL handling differences**: When they produce different results\n\n## Dialect-Specific Considerations\n\n### MySQL\n- Backtick quoting for identifiers: `table_name`.`column_name`\n- LIMIT syntax: `LIMIT n` or `LIMIT offset, count`\n- String functions: `SUBSTRING_INDEX()`, `CONCAT()`, `GROUP_CONCAT()`\n- Auto-increment: `AUTO_INCREMENT`\n- Boolean: `TINYINT(1)`, `TRUE`/`FALSE`\n\n### PostgreSQL\n- Double-quote identifiers: \"column\"\n- Type casting: `value::type` or `CAST(value AS type)`\n- Array operations: `ANY()`, `ALL()`, array literals\n- String functions: `SPLIT_PART()`, `STRING_AGG()`\n- Sequences: `SERIAL`, `BIGSERIAL`\n- Boolean: Native `BOOLEAN` type\n\n### SQLite\n- Limited type system (TEXT, INTEGER, REAL, BLOB)\n- String concatenation: `||` operator\n- No native BOOLEAN (uses 0/1)\n- `AUTOINCREMENT` keyword\n- No RIGHT JOIN or FULL OUTER JOIN\n\n## Output Format\n\nAnalyze both queries step by step, then provide your verdict:\n- If the queries are functionally equivalent: [[A=B]]\n- If the queries produce different results: [[A!=B]]\n\nExample: \"After analyzing both queries, my verdict is [[A=B]]\".", "role": "system", "type": "message"}, {"content": "<|SQL Dialect|>\npostgresql\n\n<|Database Schema and Sample Data|>\nCREATE TABLE users (\n user_id SERIAL PRIMARY KEY,\n username VARCHAR(50) UNIQUE NOT NULL,\n email VARCHAR(150) NOT NULL,\n registration_date DATE NOT NULL,\n is_premium BOOLEAN DEFAULT FALSE\n);\n\nCREATE TABLE posts (\n post_id SERIAL PRIMARY KEY,\n user_id INT REFERENCES users(user_id),\n title VARCHAR(200) NOT NULL,\n content TEXT,\n created_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP,\n view_count INT DEFAULT 0\n);\n\nCREATE TABLE comments (\n comment_id SERIAL PRIMARY KEY,\n post_id INT REFERENCES posts(post_id),\n user_id INT REFERENCES users(user_id),\n comment_text TEXT NOT NULL,\n created_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP\n);\n\nINSERT INTO users (username, email, registration_date, is_premium) VALUES\n('alice_dev', 'alice@example.com', '2024-01-15', TRUE),\n('bob_writer', 'bob@example.com', '2024-02-20', FALSE),\n('carol_blogger', 'carol@example.com', '2024-03-10', TRUE);\n\nINSERT INTO posts (user_id, title, content, view_count) VALUES\n(1, 'Getting Started with PostgreSQL', 'A beginner guide...', 1500),\n(1, 'Advanced SQL Techniques', 'Deep dive into...', 2300),\n(1, 'Database Optimization', 'Tips for performance...', 1800),\n(1, 'SQL Best Practices', 'Industry standards...', 900),\n(1, 'Query Tuning Guide', 'How to optimize...', 1200),\n(2, 'Writing Tips', 'How to write better...', 500),\n(3, 'My First Blog Post', 'Hello world!', 100);\n\nINSERT INTO comments (post_id, user_id, comment_text) VALUES\n(1, 2, 'Great introduction!'),\n(1, 3, 'Very helpful, thanks!'),\n(2, 2, 'Learned a lot from this'),\n(2, 3, 'Can you elaborate on CTEs?'),\n(3, 2, 'Nice tips!'),\n(4, 3, 'Following these now'),\n(5, 2, 'Bookmarked for later');\n\n<|Natural Language Question|>\nFind the average number of comments per post for each user, but only include users who have created at least 5 posts. Show username, total posts, total comments received, and average comments per post.\n\n<|Start of SQL Query A|>\nSELECT\n u.username,\n COUNT(p.post_id) AS total_posts,\n COALESCE(SUM(pc.comment_count), 0) AS total_comments_received,\n ROUND(COALESCE(SUM(pc.comment_count), 0)::numeric / COUNT(p.post_id), 2) AS avg_comments_per_post\nFROM\n users u\nJOIN\n posts p ON u.user_id = p.user_id\nLEFT JOIN (\n SELECT\n post_id,\n COUNT(comment_id) AS comment_count\n FROM\n comments\n GROUP BY\n post_id\n) pc ON p.post_id = pc.post_id\nGROUP BY\n u.user_id, u.username\nHAVING\n COUNT(p.post_id) >= 5\nORDER BY\n avg_comments_per_post DESC,\n u.username;\n<|End of SQL Query A|>\n\n<|Start of SQL Query B|>\nSELECT\n u.username,\n COUNT(DISTINCT p.post_id) AS total_posts,\n COUNT(c.comment_id) AS total_comments,\n ROUND(COUNT(c.comment_id)::NUMERIC / COUNT(DISTINCT p.post_id), 2) AS avg_comments_per_post\nFROM users u\nJOIN posts p ON u.user_id = p.user_id\nLEFT JOIN comments c ON p.post_id = c.post_id\nGROUP BY u.user_id, u.username\nHAVING COUNT(DISTINCT p.post_id) >= 5\nORDER BY avg_comments_per_post DESC;\n<|End of SQL Query B|>", "role": "user", "type": "message"}], "instructions": null, "max_output_tokens": null, "max_tool_calls": null, "metadata": null, "model": null, "parallel_tool_calls": true, "previous_response_id": null, "prompt": null, "reasoning": null, "service_tier": null, "store": null, "temperature": null, "text": null, "tool_choice": "auto", "tools": [], "top_logprobs": null, "top_p": null, "truncation": null, "user": null, "stream": null}, "response": {"id": "resp_098d37e07dfcc446006984e819321c819094ceedcec0d6352f", "created_at": 1770317849.0, "error": null, "incomplete_details": null, "instructions": null, "metadata": {}, "model": "gpt-4.1-2025-04-14", "object": "response", "output": [{"id": "msg_098d37e07dfcc446006984e819a064819094cc0fcbd749f1f5", "content": [{"annotations": [], "text": "Let's analyze both queries step by step, with focus on subtlety around aggregate logic and result columns. The goal is to determine whether the outputs could differ, for any valid instance of the schema.\n\n---\n\n## **Breakdown of Required Output (per NLQ)**\n\nFor **each user** who has authored at least **5 posts**:\n1. `username`\n2. `total_posts` - number of posts authored by the user\n3. `total_comments_received` - *sum of all comments received on all their posts*\n4. `avg_comments_per_post` - average number of comments per post, rounded to 2 decimals\n\n---\n\n## **Query A Analysis**\n\n### Mechanics\n- Joins each user to their posts.\n- LEFT JOIN to a subquery yielding, per post, the **count of comments** on that post.\n- After join, per user:\n - `COUNT(p.post_id)`: **Total posts** (since join is inner to posts: user *must* have posts).\n - `COALESCE(SUM(pc.comment_count), 0)`: **Sum of comments received across posts.**\n - `pc.comment_count` is COUNT of comments on each post; SUM gives total.\n - COALESCE makes 0 if user has no comments on any of their posts.\n - Average = total comments / total posts, rounded. Handles users with 0 comments.\n- Only users with >= 5 posts due to HAVING.\n\n### Key Points\n- Accurate on post count (one row per user-post; counting p.post_id is safe).\n- Accurate on comment count, as `pc.comment_count` is pre-aggregated and summed over posts.\n- Unaffected by posts with 0 comments.\n\n\n---\n\n## **Query B Analysis**\n\n### Mechanics\n- Joins users to their posts (inner).\n- LEFT JOIN to comments c (ON p.post_id = c.post_id):\n - This produces a row for each (user, post, comment on post), or (user, post, NULL) if no comments.\n- For each user:\n - `COUNT(DISTINCT p.post_id)`: **Total posts**.\n - `COUNT(c.comment_id)`: **Total comments received across all their posts**.\n - Note: COUNT(column) on a left join counts only non-NULLs\u2014so for posts with no comments, no extra count.\n - Average = counts divided, as above, rounded.\n- HAVING on post count.\n\n### Key Points\n- `COUNT(DISTINCT p.post_id)` vs `COUNT(p.post_id)` in A: Equivalent here, because users are only included if mapped to their own posts (via inner join)\u2014for a given user, each post appears at least once; DISTINCT is safer if a post could appear >1 time via join (from multiple comments), but post_id uniquely identifies a post.\n- `COUNT(c.comment_id)` over the left join: counts one for each comment on user's post. No comments = not counted (left join keeps post, but c is NULL; COUNT skips).\n- Both handle posts with 0 comments identically (included in post count, not in comment count).\n\n---\n\n## **Potential for Difference?**\n\n### What could cause a difference:\n- **Duplication in Join Rows:** In B, if a post had multiple comments, that post_id (for that user) would appear once per comment, so just `COUNT(p.post_id)` would overcount posts. But Query B uses `COUNT(DISTINCT p.post_id)`, which fixes that.\n- **SUM/COUNT of comment counts:** In A, comment count is pre-aggregated per post, so multiple comments per post don't duplicate post entries.\n- **NULL-handling:** Both handle posts with 0 comments identically, thanks to LEFT JOIN and use of COUNT/SUM/COALESCE.\n\n### Output Columns:\n- A names third column \"total_comments_received\", B names it \"total_comments\"\u2014this is cosmetic.\n- Both divide *total comments by total posts*, rounded to 2 decimals.\n\n### ORDER BY\n- Both order by `avg_comments_per_post DESC`; A adds `u.username` as secondary, but that's just tie-breaking.\n- Acceptable under the rules (\"IGNORE order by except where strictly required by NLQ or when results otherwise differ\").\n\n---\n\n## **Verdict**\n\n- For every user with \u2265 5 posts:\n - `total_posts` will be identical (COUNT(p.post_id) == COUNT(DISTINCT p.post_id))\n - `total_comments` will be identical (SUM(pc.comment_count) == COUNT(c.comment_id))\n - `avg_comments_per_post` will be identical\n\n**There is no possible schema-conforming data that makes the outputs differ.**\n\n---\n\n**After analyzing both queries, my verdict is [[A=B]].**", "type": "output_text", "logprobs": []}], "role": "assistant", "status": "completed", "type": "message"}], "parallel_tool_calls": true, "temperature": 1.0, "tool_choice": "auto", "tools": [], "top_p": 1.0, "background": false, "conversation": null, "max_output_tokens": null, "max_tool_calls": null, "previous_response_id": null, "prompt": null, "prompt_cache_key": null, "reasoning": {"effort": null, "generate_summary": null, "summary": null}, "safety_identifier": null, "service_tier": "default", "status": "completed", "text": {"format": {"type": "text"}, "verbosity": "medium"}, "top_logprobs": 0, "truncation": "disabled", "usage": {"input_tokens": 1707, "input_tokens_details": {"cached_tokens": 1024}, "output_tokens": 992, "output_tokens_details": {"reasoning_tokens": 0}, "total_tokens": 2699}, "user": null, "billing": {"payer": "developer"}, "completed_at": 1770317876, "frequency_penalty": 0.0, "presence_penalty": 0.0, "prompt_cache_retention": null, "store": true}, "verdict_label": "[[A=B]]"}], "metadata": null} +{"responses_create_params": {"background": null, "include": null, "input": [{"content": "You are a SQL assistant who expertly maps natural language commands and problems into clean, efficient, and well-structured SQL queries. You always carefully examine the database context to include only the relevant tables and fields. You return only SQL code (no explanations outside of SQL code block; only proper comments are permitted). Wrap your SQL query inside a ```sql``` code block.", "role": "system", "type": "message"}, {"content": "postgresql\n\n\nCREATE TABLE customers (\n customer_id SERIAL PRIMARY KEY,\n name VARCHAR(100) NOT NULL,\n email VARCHAR(150) UNIQUE NOT NULL,\n created_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP\n);\n\nCREATE TABLE orders (\n order_id SERIAL PRIMARY KEY,\n customer_id INT REFERENCES customers(customer_id),\n order_date DATE NOT NULL,\n total_amount DECIMAL(10,2) NOT NULL,\n status VARCHAR(20) DEFAULT 'pending'\n);\n\nINSERT INTO customers (name, email) VALUES\n('Alice Smith', 'alice@example.com'),\n('Bob Jones', 'bob@example.com'),\n('Carol White', 'carol@example.com');\n\nINSERT INTO orders (customer_id, order_date, total_amount, status) VALUES\n(1, '2025-01-15', 150.00, 'completed'),\n(1, '2025-02-20', 200.00, 'completed'),\n(1, '2025-03-10', 75.00, 'completed'),\n(1, '2025-04-05', 125.00, 'completed'),\n(2, '2025-01-20', 300.00, 'completed'),\n(2, '2025-02-25', 450.00, 'pending');\n\n\n\nFind all customers who have placed more than 3 orders with a total spending exceeding $500, showing their name, email, order count, and total spent, sorted by total spent descending.", "role": "user", "type": "message"}], "instructions": null, "max_output_tokens": null, "max_tool_calls": null, "metadata": null, "model": null, "parallel_tool_calls": true, "previous_response_id": null, "prompt": null, "reasoning": null, "service_tier": null, "store": null, "temperature": null, "text": null, "tool_choice": "auto", "tools": [], "top_logprobs": null, "top_p": null, "truncation": null, "user": null, "stream": null}, "response": {"id": "resp_05e49de260c7c3aa006985206b180081a094f35ce48a6c2644", "created_at": 1770332267.0, "error": null, "incomplete_details": null, "instructions": null, "metadata": {}, "model": "gpt-4.1-2025-04-14", "object": "response", "output": [{"id": "msg_05e49de260c7c3aa006985206b906481a0a05a88a7475b8aaf", "content": [{"annotations": [], "text": "```sql\nSELECT\n c.name,\n c.email,\n COUNT(o.order_id) AS order_count,\n SUM(o.total_amount) AS total_spent\nFROM\n customers c\nJOIN\n orders o ON c.customer_id = o.customer_id\nGROUP BY\n c.customer_id, c.name, c.email\nHAVING\n COUNT(o.order_id) > 3\n AND SUM(o.total_amount) > 500\nORDER BY\n total_spent DESC;\n```", "type": "output_text", "logprobs": []}], "role": "assistant", "status": "completed", "type": "message"}], "parallel_tool_calls": true, "temperature": 1.0, "tool_choice": "auto", "tools": [], "top_p": 1.0, "background": false, "conversation": null, "max_output_tokens": null, "max_tool_calls": null, "previous_response_id": null, "prompt": null, "prompt_cache_key": null, "reasoning": {"effort": null, "generate_summary": null, "summary": null}, "safety_identifier": null, "service_tier": "default", "status": "completed", "text": {"format": {"type": "text"}, "verbosity": "medium"}, "top_logprobs": 0, "truncation": "disabled", "usage": {"input_tokens": 406, "input_tokens_details": {"cached_tokens": 0}, "output_tokens": 103, "output_tokens_details": {"reasoning_tokens": 0}, "total_tokens": 509}, "user": null, "billing": {"payer": "developer"}, "completed_at": 1770332268, "frequency_penalty": 0.0, "presence_penalty": 0.0, "prompt_cache_retention": null, "store": true}, "reward": 1.0, "uuid": "ecommerce-001", "sql": "SELECT\n c.name,\n c.email,\n COUNT(o.order_id) AS order_count,\n SUM(o.total_amount) AS total_spent\nFROM customers c\nJOIN orders o ON c.customer_id = o.customer_id\nGROUP BY c.customer_id, c.name, c.email\nHAVING COUNT(o.order_id) > 3 AND SUM(o.total_amount) > 500\nORDER BY total_spent DESC;", "model_output": "```sql\nSELECT\n c.name,\n c.email,\n COUNT(o.order_id) AS order_count,\n SUM(o.total_amount) AS total_spent\nFROM\n customers c\nJOIN\n orders o ON c.customer_id = o.customer_id\nGROUP BY\n c.customer_id, c.name, c.email\nHAVING\n COUNT(o.order_id) > 3\n AND SUM(o.total_amount) > 500\nORDER BY\n total_spent DESC;\n```", "extracted_sql": "SELECT\n c.name,\n c.email,\n COUNT(o.order_id) AS order_count,\n SUM(o.total_amount) AS total_spent\nFROM\n customers c\nJOIN\n orders o ON c.customer_id = o.customer_id\nGROUP BY\n c.customer_id, c.name, c.email\nHAVING\n COUNT(o.order_id) > 3\n AND SUM(o.total_amount) > 500\nORDER BY\n total_spent DESC;", "sql_dialect": "postgresql", "sql_context": "CREATE TABLE customers (\n customer_id SERIAL PRIMARY KEY,\n name VARCHAR(100) NOT NULL,\n email VARCHAR(150) UNIQUE NOT NULL,\n created_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP\n);\n\nCREATE TABLE orders (\n order_id SERIAL PRIMARY KEY,\n customer_id INT REFERENCES customers(customer_id),\n order_date DATE NOT NULL,\n total_amount DECIMAL(10,2) NOT NULL,\n status VARCHAR(20) DEFAULT 'pending'\n);\n\nINSERT INTO customers (name, email) VALUES\n('Alice Smith', 'alice@example.com'),\n('Bob Jones', 'bob@example.com'),\n('Carol White', 'carol@example.com');\n\nINSERT INTO orders (customer_id, order_date, total_amount, status) VALUES\n(1, '2025-01-15', 150.00, 'completed'),\n(1, '2025-02-20', 200.00, 'completed'),\n(1, '2025-03-10', 75.00, 'completed'),\n(1, '2025-04-05', 125.00, 'completed'),\n(2, '2025-01-20', 300.00, 'completed'),\n(2, '2025-02-25', 450.00, 'pending');", "sql_prompt": "Find all customers who have placed more than 3 orders with a total spending exceeding $500, showing their name, email, order count, and total spent, sorted by total spent descending.", "judge_passed": true, "failure_reason": "none", "judge_evaluations": [{"responses_create_params": {"background": null, "include": null, "input": [{"content": "You are an expert SQL judge evaluating the functional equivalence of two SQL queries. You will be given SQL query A and SQL query B. Your task is to determine whether both queries would produce identical result sets when executed against the provided database schema.\n\n## Core Evaluation Principle\n\nTwo SQL queries are functionally equivalent if and only if they return the same rows with the same column values for all possible database states conforming to the schema. Focus on semantic equivalence, not syntactic similarity.\n\n## SQL Equivalence Rules\n\n### Always Equivalent (ignore these differences):\n- **Formatting**: Whitespace, line breaks, capitalization of keywords\n- **Aliasing**: Different table/column aliases (e.g., `e.name` vs `employees.name`)\n- **JOIN syntax**: `FROM a, b WHERE a.id = b.id` \u2261 `FROM a JOIN b ON a.id = b.id`\n- **JOIN keyword**: `JOIN` \u2261 `INNER JOIN`\n- **Explicit vs implicit column lists**: When results are identical\n- **Redundant DISTINCT**: When the query already guarantees uniqueness (e.g., selecting a PRIMARY KEY)\n- **Parentheses**: Extra parentheses that don't change evaluation order\n- **Semicolons**: Trailing semicolon presence/absence\n\n### Equivalent Patterns (require careful analysis):\n- **CTE vs Subquery**: `WITH t AS (SELECT ...) SELECT * FROM t` may equal a nested subquery\n- **EXISTS vs IN vs JOIN**: Often equivalent for the same filtering logic\n- **COALESCE vs CASE**: `COALESCE(a, b)` \u2261 `CASE WHEN a IS NOT NULL THEN a ELSE b END`\n- **Boolean expressions**: `NOT (a AND b)` \u2261 `(NOT a) OR (NOT b)` (De Morgan's laws)\n- **Arithmetic**: `price * 1.1` \u2261 `price + price * 0.1`\n- **Date arithmetic**: Dialect-specific but logically equivalent date calculations\n\n### Never Equivalent (these always matter):\n- **Different columns selected**: `SELECT a, b` \u2260 `SELECT a, c`\n- **Different filtering**: `WHERE x > 5` \u2260 `WHERE x >= 5`\n- **Different aggregations**: `SUM(x)` \u2260 `AVG(x)`\n- **Different grouping**: `GROUP BY a` \u2260 `GROUP BY a, b`\n- **Missing/extra DISTINCT**: When it changes the result set\n- **Different ORDER BY**: When ordering is semantically required by the question\n- **Different LIMIT/OFFSET**: When they affect the result set\n- **NULL handling differences**: When they produce different results\n\n## Dialect-Specific Considerations\n\n### MySQL\n- Backtick quoting for identifiers: `table_name`.`column_name`\n- LIMIT syntax: `LIMIT n` or `LIMIT offset, count`\n- String functions: `SUBSTRING_INDEX()`, `CONCAT()`, `GROUP_CONCAT()`\n- Auto-increment: `AUTO_INCREMENT`\n- Boolean: `TINYINT(1)`, `TRUE`/`FALSE`\n\n### PostgreSQL\n- Double-quote identifiers: \"column\"\n- Type casting: `value::type` or `CAST(value AS type)`\n- Array operations: `ANY()`, `ALL()`, array literals\n- String functions: `SPLIT_PART()`, `STRING_AGG()`\n- Sequences: `SERIAL`, `BIGSERIAL`\n- Boolean: Native `BOOLEAN` type\n\n### SQLite\n- Limited type system (TEXT, INTEGER, REAL, BLOB)\n- String concatenation: `||` operator\n- No native BOOLEAN (uses 0/1)\n- `AUTOINCREMENT` keyword\n- No RIGHT JOIN or FULL OUTER JOIN\n\n## Output Format\n\nAnalyze both queries step by step, then provide your verdict:\n- If the queries are functionally equivalent: [[A=B]]\n- If the queries produce different results: [[A!=B]]\n\nExample: \"After analyzing both queries, my verdict is [[A=B]]\".", "role": "system", "type": "message"}, {"content": "<|SQL Dialect|>\npostgresql\n\n<|Database Schema and Sample Data|>\nCREATE TABLE customers (\n customer_id SERIAL PRIMARY KEY,\n name VARCHAR(100) NOT NULL,\n email VARCHAR(150) UNIQUE NOT NULL,\n created_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP\n);\n\nCREATE TABLE orders (\n order_id SERIAL PRIMARY KEY,\n customer_id INT REFERENCES customers(customer_id),\n order_date DATE NOT NULL,\n total_amount DECIMAL(10,2) NOT NULL,\n status VARCHAR(20) DEFAULT 'pending'\n);\n\nINSERT INTO customers (name, email) VALUES\n('Alice Smith', 'alice@example.com'),\n('Bob Jones', 'bob@example.com'),\n('Carol White', 'carol@example.com');\n\nINSERT INTO orders (customer_id, order_date, total_amount, status) VALUES\n(1, '2025-01-15', 150.00, 'completed'),\n(1, '2025-02-20', 200.00, 'completed'),\n(1, '2025-03-10', 75.00, 'completed'),\n(1, '2025-04-05', 125.00, 'completed'),\n(2, '2025-01-20', 300.00, 'completed'),\n(2, '2025-02-25', 450.00, 'pending');\n\n<|Natural Language Question|>\nFind all customers who have placed more than 3 orders with a total spending exceeding $500, showing their name, email, order count, and total spent, sorted by total spent descending.\n\n<|Start of SQL Query A|>\nSELECT\n c.name,\n c.email,\n COUNT(o.order_id) AS order_count,\n SUM(o.total_amount) AS total_spent\nFROM customers c\nJOIN orders o ON c.customer_id = o.customer_id\nGROUP BY c.customer_id, c.name, c.email\nHAVING COUNT(o.order_id) > 3 AND SUM(o.total_amount) > 500\nORDER BY total_spent DESC;\n<|End of SQL Query A|>\n\n<|Start of SQL Query B|>\nSELECT\n c.name,\n c.email,\n COUNT(o.order_id) AS order_count,\n SUM(o.total_amount) AS total_spent\nFROM\n customers c\nJOIN\n orders o ON c.customer_id = o.customer_id\nGROUP BY\n c.customer_id, c.name, c.email\nHAVING\n COUNT(o.order_id) > 3\n AND SUM(o.total_amount) > 500\nORDER BY\n total_spent DESC;\n<|End of SQL Query B|>", "role": "user", "type": "message"}], "instructions": null, "max_output_tokens": null, "max_tool_calls": null, "metadata": null, "model": null, "parallel_tool_calls": true, "previous_response_id": null, "prompt": null, "reasoning": null, "service_tier": null, "store": null, "temperature": null, "text": null, "tool_choice": "auto", "tools": [], "top_logprobs": null, "top_p": null, "truncation": null, "user": null, "stream": null}, "response": {"id": "resp_0b7d95a691f02a37006985206d0fd081959039da46569195ca", "created_at": 1770332269.0, "error": null, "incomplete_details": null, "instructions": null, "metadata": {}, "model": "gpt-4.1-2025-04-14", "object": "response", "output": [{"id": "msg_0b7d95a691f02a37006985206db2048195a9174ac5e9ccc5a1", "content": [{"annotations": [], "text": "Let's analyze both queries step by step.\n\n### Query A\n\n```sql\nSELECT\n c.name,\n c.email,\n COUNT(o.order_id) AS order_count,\n SUM(o.total_amount) AS total_spent\nFROM customers c\nJOIN orders o ON c.customer_id = o.customer_id\nGROUP BY c.customer_id, c.name, c.email\nHAVING COUNT(o.order_id) > 3 AND SUM(o.total_amount) > 500\nORDER BY total_spent DESC;\n```\n\n### Query B\n\n```sql\nSELECT\n c.name,\n c.email,\n COUNT(o.order_id) AS order_count,\n SUM(o.total_amount) AS total_spent\nFROM\n customers c\nJOIN\n orders o ON c.customer_id = o.customer_id\nGROUP BY\n c.customer_id, c.name, c.email\nHAVING\n COUNT(o.order_id) > 3\n AND SUM(o.total_amount) > 500\nORDER BY\n total_spent DESC;\n```\n\n---\n\n## Step-by-step Comparison\n\n1. **SELECT columns**: Both select `c.name`, `c.email`, `COUNT(o.order_id) AS order_count`, `SUM(o.total_amount) AS total_spent`.\n - Identical.\n\n2. **FROM/JOIN**: Both do an inner join between `customers` and `orders` on `customer_id`.\n - Identical.\n\n3. **GROUP BY**: Both group by `c.customer_id, c.name, c.email`.\n - Identical (repeating these three columns, which is common for unique grouping).\n\n4. **HAVING clause**: Both require:\n - `COUNT(o.order_id) > 3`\n - `SUM(o.total_amount) > 500`\n - Same conditions, just split over two lines in the second, but identical logic.\n\n5. **ORDER BY**: Both order by `total_spent DESC`.\n - Identical.\n\n6. **Syntactic Differences**: Only in whitespace and line breaks.\n\n---\n\n## Core Semantics\n\n- Both queries return a row for each customer who has placed more than 3 orders **and** whose total spending exceeds $500.\n- The result shows name, email, # of orders, and total spent, sorted descending by total spent.\n\n---\n\n## Verdict\n\nThere are NO semantic or functional differences between Query A and Query B; they are identical except for whitespace.\n\n**After analyzing both queries, my verdict is [[A=B]].**", "type": "output_text", "logprobs": []}], "role": "assistant", "status": "completed", "type": "message"}], "parallel_tool_calls": true, "temperature": 1.0, "tool_choice": "auto", "tools": [], "top_p": 1.0, "background": false, "conversation": null, "max_output_tokens": null, "max_tool_calls": null, "previous_response_id": null, "prompt": null, "prompt_cache_key": null, "reasoning": {"effort": null, "generate_summary": null, "summary": null}, "safety_identifier": null, "service_tier": "default", "status": "completed", "text": {"format": {"type": "text"}, "verbosity": "medium"}, "top_logprobs": 0, "truncation": "disabled", "usage": {"input_tokens": 1408, "input_tokens_details": {"cached_tokens": 0}, "output_tokens": 515, "output_tokens_details": {"reasoning_tokens": 0}, "total_tokens": 1923}, "user": null, "billing": {"payer": "developer"}, "completed_at": 1770332275, "frequency_penalty": 0.0, "presence_penalty": 0.0, "prompt_cache_retention": null, "store": true}, "verdict_label": "[[A=B]]"}, {"responses_create_params": {"background": null, "include": null, "input": [{"content": "You are an expert SQL judge evaluating the functional equivalence of two SQL queries. You will be given SQL query A and SQL query B. Your task is to determine whether both queries would produce identical result sets when executed against the provided database schema.\n\n## Core Evaluation Principle\n\nTwo SQL queries are functionally equivalent if and only if they return the same rows with the same column values for all possible database states conforming to the schema. Focus on semantic equivalence, not syntactic similarity.\n\n## SQL Equivalence Rules\n\n### Always Equivalent (ignore these differences):\n- **Formatting**: Whitespace, line breaks, capitalization of keywords\n- **Aliasing**: Different table/column aliases (e.g., `e.name` vs `employees.name`)\n- **JOIN syntax**: `FROM a, b WHERE a.id = b.id` \u2261 `FROM a JOIN b ON a.id = b.id`\n- **JOIN keyword**: `JOIN` \u2261 `INNER JOIN`\n- **Explicit vs implicit column lists**: When results are identical\n- **Redundant DISTINCT**: When the query already guarantees uniqueness (e.g., selecting a PRIMARY KEY)\n- **Parentheses**: Extra parentheses that don't change evaluation order\n- **Semicolons**: Trailing semicolon presence/absence\n\n### Equivalent Patterns (require careful analysis):\n- **CTE vs Subquery**: `WITH t AS (SELECT ...) SELECT * FROM t` may equal a nested subquery\n- **EXISTS vs IN vs JOIN**: Often equivalent for the same filtering logic\n- **COALESCE vs CASE**: `COALESCE(a, b)` \u2261 `CASE WHEN a IS NOT NULL THEN a ELSE b END`\n- **Boolean expressions**: `NOT (a AND b)` \u2261 `(NOT a) OR (NOT b)` (De Morgan's laws)\n- **Arithmetic**: `price * 1.1` \u2261 `price + price * 0.1`\n- **Date arithmetic**: Dialect-specific but logically equivalent date calculations\n\n### Never Equivalent (these always matter):\n- **Different columns selected**: `SELECT a, b` \u2260 `SELECT a, c`\n- **Different filtering**: `WHERE x > 5` \u2260 `WHERE x >= 5`\n- **Different aggregations**: `SUM(x)` \u2260 `AVG(x)`\n- **Different grouping**: `GROUP BY a` \u2260 `GROUP BY a, b`\n- **Missing/extra DISTINCT**: When it changes the result set\n- **Different ORDER BY**: When ordering is semantically required by the question\n- **Different LIMIT/OFFSET**: When they affect the result set\n- **NULL handling differences**: When they produce different results\n\n## Dialect-Specific Considerations\n\n### MySQL\n- Backtick quoting for identifiers: `table_name`.`column_name`\n- LIMIT syntax: `LIMIT n` or `LIMIT offset, count`\n- String functions: `SUBSTRING_INDEX()`, `CONCAT()`, `GROUP_CONCAT()`\n- Auto-increment: `AUTO_INCREMENT`\n- Boolean: `TINYINT(1)`, `TRUE`/`FALSE`\n\n### PostgreSQL\n- Double-quote identifiers: \"column\"\n- Type casting: `value::type` or `CAST(value AS type)`\n- Array operations: `ANY()`, `ALL()`, array literals\n- String functions: `SPLIT_PART()`, `STRING_AGG()`\n- Sequences: `SERIAL`, `BIGSERIAL`\n- Boolean: Native `BOOLEAN` type\n\n### SQLite\n- Limited type system (TEXT, INTEGER, REAL, BLOB)\n- String concatenation: `||` operator\n- No native BOOLEAN (uses 0/1)\n- `AUTOINCREMENT` keyword\n- No RIGHT JOIN or FULL OUTER JOIN\n\n## Output Format\n\nAnalyze both queries step by step, then provide your verdict:\n- If the queries are functionally equivalent: [[A=B]]\n- If the queries produce different results: [[A!=B]]\n\nExample: \"After analyzing both queries, my verdict is [[A=B]]\".", "role": "system", "type": "message"}, {"content": "<|SQL Dialect|>\npostgresql\n\n<|Database Schema and Sample Data|>\nCREATE TABLE customers (\n customer_id SERIAL PRIMARY KEY,\n name VARCHAR(100) NOT NULL,\n email VARCHAR(150) UNIQUE NOT NULL,\n created_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP\n);\n\nCREATE TABLE orders (\n order_id SERIAL PRIMARY KEY,\n customer_id INT REFERENCES customers(customer_id),\n order_date DATE NOT NULL,\n total_amount DECIMAL(10,2) NOT NULL,\n status VARCHAR(20) DEFAULT 'pending'\n);\n\nINSERT INTO customers (name, email) VALUES\n('Alice Smith', 'alice@example.com'),\n('Bob Jones', 'bob@example.com'),\n('Carol White', 'carol@example.com');\n\nINSERT INTO orders (customer_id, order_date, total_amount, status) VALUES\n(1, '2025-01-15', 150.00, 'completed'),\n(1, '2025-02-20', 200.00, 'completed'),\n(1, '2025-03-10', 75.00, 'completed'),\n(1, '2025-04-05', 125.00, 'completed'),\n(2, '2025-01-20', 300.00, 'completed'),\n(2, '2025-02-25', 450.00, 'pending');\n\n<|Natural Language Question|>\nFind all customers who have placed more than 3 orders with a total spending exceeding $500, showing their name, email, order count, and total spent, sorted by total spent descending.\n\n<|Start of SQL Query A|>\nSELECT\n c.name,\n c.email,\n COUNT(o.order_id) AS order_count,\n SUM(o.total_amount) AS total_spent\nFROM\n customers c\nJOIN\n orders o ON c.customer_id = o.customer_id\nGROUP BY\n c.customer_id, c.name, c.email\nHAVING\n COUNT(o.order_id) > 3\n AND SUM(o.total_amount) > 500\nORDER BY\n total_spent DESC;\n<|End of SQL Query A|>\n\n<|Start of SQL Query B|>\nSELECT\n c.name,\n c.email,\n COUNT(o.order_id) AS order_count,\n SUM(o.total_amount) AS total_spent\nFROM customers c\nJOIN orders o ON c.customer_id = o.customer_id\nGROUP BY c.customer_id, c.name, c.email\nHAVING COUNT(o.order_id) > 3 AND SUM(o.total_amount) > 500\nORDER BY total_spent DESC;\n<|End of SQL Query B|>", "role": "user", "type": "message"}], "instructions": null, "max_output_tokens": null, "max_tool_calls": null, "metadata": null, "model": null, "parallel_tool_calls": true, "previous_response_id": null, "prompt": null, "reasoning": null, "service_tier": null, "store": null, "temperature": null, "text": null, "tool_choice": "auto", "tools": [], "top_logprobs": null, "top_p": null, "truncation": null, "user": null, "stream": null}, "response": {"id": "resp_032640331bf428b500698520741b7c819d9ea4c4b328eb6da6", "created_at": 1770332276.0, "error": null, "incomplete_details": null, "instructions": null, "metadata": {}, "model": "gpt-4.1-2025-04-14", "object": "response", "output": [{"id": "msg_032640331bf428b500698520747760819d87b9d1ebbc42ba49", "content": [{"annotations": [], "text": "Let's analyze both queries step by step.\n\n## Query A\n\n```sql\nSELECT\n c.name,\n c.email,\n COUNT(o.order_id) AS order_count,\n SUM(o.total_amount) AS total_spent\nFROM\n customers c\nJOIN\n orders o ON c.customer_id = o.customer_id\nGROUP BY\n c.customer_id, c.name, c.email\nHAVING\n COUNT(o.order_id) > 3\n AND SUM(o.total_amount) > 500\nORDER BY\n total_spent DESC;\n```\n\n## Query B\n\n```sql\nSELECT\n c.name,\n c.email,\n COUNT(o.order_id) AS order_count,\n SUM(o.total_amount) AS total_spent\nFROM customers c\nJOIN orders o ON c.customer_id = o.customer_id\nGROUP BY c.customer_id, c.name, c.email\nHAVING COUNT(o.order_id) > 3 AND SUM(o.total_amount) > 500\nORDER BY total_spent DESC;\n```\n\n## Step-by-Step Analysis\n\n### 1. FROM/JOIN Clauses\n- Both queries use an inner join of `customers` to `orders` on `customer_id`. Identical.\n\n### 2. SELECT List\n- Both select: `name`, `email`, `COUNT(o.order_id) AS order_count`, `SUM(o.total_amount) AS total_spent`.\n- Aliasing is identical.\n\n### 3. GROUP BY Clause\n- Both group by `c.customer_id, c.name, c.email`. Identical.\n- Including `c.customer_id` (which is unique) ensures group uniqueness.\n\n### 4. HAVING Clause\n- Both use: `COUNT(o.order_id) > 3 AND SUM(o.total_amount) > 500`\n- This filters for customers with more than 3 orders and whose total spending exceeds $500.\n\n### 5. ORDER BY Clause\n- Both order by `total_spent DESC`.\n\n### 6. Syntax/Formatting\n- Query B condenses its syntax (no extra whitespace, different line breaks) but this does not affect semantic meaning or result set.\n\n## Schema Effects\n- No effect on equivalence; both queries use only standard columns, aggregations, and grouping as defined in schema.\n\n## Conclusion\n\nAfter full analysis, every component of Query A and Query B is identical in terms of logic and behavior for all possible database states. All differences are purely in formatting/whitespace.\n\n**Final Verdict:** \nAfter analyzing both queries, my verdict is [[A=B]].", "type": "output_text", "logprobs": []}], "role": "assistant", "status": "completed", "type": "message"}], "parallel_tool_calls": true, "temperature": 1.0, "tool_choice": "auto", "tools": [], "top_p": 1.0, "background": false, "conversation": null, "max_output_tokens": null, "max_tool_calls": null, "previous_response_id": null, "prompt": null, "prompt_cache_key": null, "reasoning": {"effort": null, "generate_summary": null, "summary": null}, "safety_identifier": null, "service_tier": "default", "status": "completed", "text": {"format": {"type": "text"}, "verbosity": "medium"}, "top_logprobs": 0, "truncation": "disabled", "usage": {"input_tokens": 1408, "input_tokens_details": {"cached_tokens": 1024}, "output_tokens": 523, "output_tokens_details": {"reasoning_tokens": 0}, "total_tokens": 1931}, "user": null, "billing": {"payer": "developer"}, "completed_at": 1770332282, "frequency_penalty": 0.0, "presence_penalty": 0.0, "prompt_cache_retention": null, "store": true}, "verdict_label": "[[A=B]]"}], "metadata": null} +{"responses_create_params": {"background": null, "include": null, "input": [{"content": "You are a SQL assistant who expertly maps natural language commands and problems into clean, efficient, and well-structured SQL queries. You always carefully examine the database context to include only the relevant tables and fields. You return only SQL code (no explanations outside of SQL code block; only proper comments are permitted). Wrap your SQL query inside a ```sql``` code block.", "role": "system", "type": "message"}, {"content": "mysql\n\n\nCREATE TABLE products (\n product_id INT UNSIGNED AUTO_INCREMENT PRIMARY KEY,\n product_name VARCHAR(200) NOT NULL,\n category VARCHAR(50) NOT NULL,\n price DECIMAL(10,2) NOT NULL,\n stock_quantity INT NOT NULL DEFAULT 0,\n created_at DATETIME DEFAULT CURRENT_TIMESTAMP\n) ENGINE=InnoDB;\n\nCREATE TABLE sales (\n sale_id INT UNSIGNED AUTO_INCREMENT PRIMARY KEY,\n product_id INT UNSIGNED NOT NULL,\n sale_date DATE NOT NULL,\n quantity_sold INT NOT NULL,\n revenue DECIMAL(10,2) NOT NULL,\n FOREIGN KEY (product_id) REFERENCES products(product_id)\n) ENGINE=InnoDB;\n\nINSERT INTO products (product_name, category, price, stock_quantity) VALUES\n('Laptop Pro', 'Electronics', 1299.99, 50),\n('Wireless Mouse', 'Electronics', 29.99, 200),\n('Office Chair', 'Furniture', 249.99, 75),\n('Standing Desk', 'Furniture', 599.99, 30),\n('USB Hub', 'Electronics', 39.99, 150);\n\nINSERT INTO sales (product_id, sale_date, quantity_sold, revenue) VALUES\n(1, '2025-01-15', 10, 12999.90),\n(2, '2025-01-15', 50, 1499.50),\n(1, '2025-02-10', 8, 10399.92),\n(3, '2025-02-15', 20, 4999.80),\n(4, '2025-02-20', 5, 2999.95),\n(2, '2025-03-01', 100, 2999.00),\n(5, '2025-03-05', 75, 2999.25);\n\n\n\nFor each product category, find the top-selling product (by total quantity sold) and show the category, product name, and total units sold.", "role": "user", "type": "message"}], "instructions": null, "max_output_tokens": null, "max_tool_calls": null, "metadata": null, "model": null, "parallel_tool_calls": true, "previous_response_id": null, "prompt": null, "reasoning": null, "service_tier": null, "store": null, "temperature": null, "text": null, "tool_choice": "auto", "tools": [], "top_logprobs": null, "top_p": null, "truncation": null, "user": null, "stream": null}, "response": {"id": "resp_008d52eebf50ccb3006985206b16b081928894610088126fa6", "created_at": 1770332267.0, "error": null, "incomplete_details": null, "instructions": null, "metadata": {}, "model": "gpt-4.1-2025-04-14", "object": "response", "output": [{"id": "msg_008d52eebf50ccb3006985206bb20c81929dd0ecca907370d8", "content": [{"annotations": [], "text": "```sql\nSELECT \n p.category,\n p.product_name,\n t.total_units_sold\nFROM (\n SELECT \n p.category,\n p.product_id,\n SUM(s.quantity_sold) AS total_units_sold,\n RANK() OVER (PARTITION BY p.category ORDER BY SUM(s.quantity_sold) DESC) AS rnk\n FROM products p\n JOIN sales s ON p.product_id = s.product_id\n GROUP BY p.category, p.product_id\n) t\nJOIN products p ON p.product_id = t.product_id\nWHERE t.rnk = 1;\n```", "type": "output_text", "logprobs": []}], "role": "assistant", "status": "completed", "type": "message"}], "parallel_tool_calls": true, "temperature": 1.0, "tool_choice": "auto", "tools": [], "top_p": 1.0, "background": false, "conversation": null, "max_output_tokens": null, "max_tool_calls": null, "previous_response_id": null, "prompt": null, "prompt_cache_key": null, "reasoning": {"effort": null, "generate_summary": null, "summary": null}, "safety_identifier": null, "service_tier": "default", "status": "completed", "text": {"format": {"type": "text"}, "verbosity": "medium"}, "top_logprobs": 0, "truncation": "disabled", "usage": {"input_tokens": 531, "input_tokens_details": {"cached_tokens": 0}, "output_tokens": 126, "output_tokens_details": {"reasoning_tokens": 0}, "total_tokens": 657}, "user": null, "billing": {"payer": "developer"}, "completed_at": 1770332269, "frequency_penalty": 0.0, "presence_penalty": 0.0, "prompt_cache_retention": null, "store": true}, "reward": 0.0, "uuid": "sales-analytics-001", "sql": "WITH category_sales AS (\n SELECT\n p.category,\n p.product_id,\n p.product_name,\n SUM(s.quantity_sold) AS total_sold,\n ROW_NUMBER() OVER (PARTITION BY p.category ORDER BY SUM(s.quantity_sold) DESC) AS rn\n FROM products p\n JOIN sales s ON p.product_id = s.product_id\n GROUP BY p.category, p.product_id, p.product_name\n)\nSELECT category, product_name, total_sold\nFROM category_sales\nWHERE rn = 1;", "model_output": "```sql\nSELECT \n p.category,\n p.product_name,\n t.total_units_sold\nFROM (\n SELECT \n p.category,\n p.product_id,\n SUM(s.quantity_sold) AS total_units_sold,\n RANK() OVER (PARTITION BY p.category ORDER BY SUM(s.quantity_sold) DESC) AS rnk\n FROM products p\n JOIN sales s ON p.product_id = s.product_id\n GROUP BY p.category, p.product_id\n) t\nJOIN products p ON p.product_id = t.product_id\nWHERE t.rnk = 1;\n```", "extracted_sql": "SELECT \n p.category,\n p.product_name,\n t.total_units_sold\nFROM (\n SELECT \n p.category,\n p.product_id,\n SUM(s.quantity_sold) AS total_units_sold,\n RANK() OVER (PARTITION BY p.category ORDER BY SUM(s.quantity_sold) DESC) AS rnk\n FROM products p\n JOIN sales s ON p.product_id = s.product_id\n GROUP BY p.category, p.product_id\n) t\nJOIN products p ON p.product_id = t.product_id\nWHERE t.rnk = 1;", "sql_dialect": "mysql", "sql_context": "CREATE TABLE products (\n product_id INT UNSIGNED AUTO_INCREMENT PRIMARY KEY,\n product_name VARCHAR(200) NOT NULL,\n category VARCHAR(50) NOT NULL,\n price DECIMAL(10,2) NOT NULL,\n stock_quantity INT NOT NULL DEFAULT 0,\n created_at DATETIME DEFAULT CURRENT_TIMESTAMP\n) ENGINE=InnoDB;\n\nCREATE TABLE sales (\n sale_id INT UNSIGNED AUTO_INCREMENT PRIMARY KEY,\n product_id INT UNSIGNED NOT NULL,\n sale_date DATE NOT NULL,\n quantity_sold INT NOT NULL,\n revenue DECIMAL(10,2) NOT NULL,\n FOREIGN KEY (product_id) REFERENCES products(product_id)\n) ENGINE=InnoDB;\n\nINSERT INTO products (product_name, category, price, stock_quantity) VALUES\n('Laptop Pro', 'Electronics', 1299.99, 50),\n('Wireless Mouse', 'Electronics', 29.99, 200),\n('Office Chair', 'Furniture', 249.99, 75),\n('Standing Desk', 'Furniture', 599.99, 30),\n('USB Hub', 'Electronics', 39.99, 150);\n\nINSERT INTO sales (product_id, sale_date, quantity_sold, revenue) VALUES\n(1, '2025-01-15', 10, 12999.90),\n(2, '2025-01-15', 50, 1499.50),\n(1, '2025-02-10', 8, 10399.92),\n(3, '2025-02-15', 20, 4999.80),\n(4, '2025-02-20', 5, 2999.95),\n(2, '2025-03-01', 100, 2999.00),\n(5, '2025-03-05', 75, 2999.25);", "sql_prompt": "For each product category, find the top-selling product (by total quantity sold) and show the category, product name, and total units sold.", "judge_passed": false, "failure_reason": "judge_evaluation_failed", "judge_evaluations": [{"responses_create_params": {"background": null, "include": null, "input": [{"content": "You are an expert SQL judge evaluating the functional equivalence of two SQL queries. You will be given SQL query A and SQL query B. Your task is to determine whether both queries would produce identical result sets when executed against the provided database schema.\n\n## Core Evaluation Principle\n\nTwo SQL queries are functionally equivalent if and only if they return the same rows with the same column values for all possible database states conforming to the schema. Focus on semantic equivalence, not syntactic similarity.\n\n## SQL Equivalence Rules\n\n### Always Equivalent (ignore these differences):\n- **Formatting**: Whitespace, line breaks, capitalization of keywords\n- **Aliasing**: Different table/column aliases (e.g., `e.name` vs `employees.name`)\n- **JOIN syntax**: `FROM a, b WHERE a.id = b.id` \u2261 `FROM a JOIN b ON a.id = b.id`\n- **JOIN keyword**: `JOIN` \u2261 `INNER JOIN`\n- **Explicit vs implicit column lists**: When results are identical\n- **Redundant DISTINCT**: When the query already guarantees uniqueness (e.g., selecting a PRIMARY KEY)\n- **Parentheses**: Extra parentheses that don't change evaluation order\n- **Semicolons**: Trailing semicolon presence/absence\n\n### Equivalent Patterns (require careful analysis):\n- **CTE vs Subquery**: `WITH t AS (SELECT ...) SELECT * FROM t` may equal a nested subquery\n- **EXISTS vs IN vs JOIN**: Often equivalent for the same filtering logic\n- **COALESCE vs CASE**: `COALESCE(a, b)` \u2261 `CASE WHEN a IS NOT NULL THEN a ELSE b END`\n- **Boolean expressions**: `NOT (a AND b)` \u2261 `(NOT a) OR (NOT b)` (De Morgan's laws)\n- **Arithmetic**: `price * 1.1` \u2261 `price + price * 0.1`\n- **Date arithmetic**: Dialect-specific but logically equivalent date calculations\n\n### Never Equivalent (these always matter):\n- **Different columns selected**: `SELECT a, b` \u2260 `SELECT a, c`\n- **Different filtering**: `WHERE x > 5` \u2260 `WHERE x >= 5`\n- **Different aggregations**: `SUM(x)` \u2260 `AVG(x)`\n- **Different grouping**: `GROUP BY a` \u2260 `GROUP BY a, b`\n- **Missing/extra DISTINCT**: When it changes the result set\n- **Different ORDER BY**: When ordering is semantically required by the question\n- **Different LIMIT/OFFSET**: When they affect the result set\n- **NULL handling differences**: When they produce different results\n\n## Dialect-Specific Considerations\n\n### MySQL\n- Backtick quoting for identifiers: `table_name`.`column_name`\n- LIMIT syntax: `LIMIT n` or `LIMIT offset, count`\n- String functions: `SUBSTRING_INDEX()`, `CONCAT()`, `GROUP_CONCAT()`\n- Auto-increment: `AUTO_INCREMENT`\n- Boolean: `TINYINT(1)`, `TRUE`/`FALSE`\n\n### PostgreSQL\n- Double-quote identifiers: \"column\"\n- Type casting: `value::type` or `CAST(value AS type)`\n- Array operations: `ANY()`, `ALL()`, array literals\n- String functions: `SPLIT_PART()`, `STRING_AGG()`\n- Sequences: `SERIAL`, `BIGSERIAL`\n- Boolean: Native `BOOLEAN` type\n\n### SQLite\n- Limited type system (TEXT, INTEGER, REAL, BLOB)\n- String concatenation: `||` operator\n- No native BOOLEAN (uses 0/1)\n- `AUTOINCREMENT` keyword\n- No RIGHT JOIN or FULL OUTER JOIN\n\n## Output Format\n\nAnalyze both queries step by step, then provide your verdict:\n- If the queries are functionally equivalent: [[A=B]]\n- If the queries produce different results: [[A!=B]]\n\nExample: \"After analyzing both queries, my verdict is [[A=B]]\".", "role": "system", "type": "message"}, {"content": "<|SQL Dialect|>\nmysql\n\n<|Database Schema and Sample Data|>\nCREATE TABLE products (\n product_id INT UNSIGNED AUTO_INCREMENT PRIMARY KEY,\n product_name VARCHAR(200) NOT NULL,\n category VARCHAR(50) NOT NULL,\n price DECIMAL(10,2) NOT NULL,\n stock_quantity INT NOT NULL DEFAULT 0,\n created_at DATETIME DEFAULT CURRENT_TIMESTAMP\n) ENGINE=InnoDB;\n\nCREATE TABLE sales (\n sale_id INT UNSIGNED AUTO_INCREMENT PRIMARY KEY,\n product_id INT UNSIGNED NOT NULL,\n sale_date DATE NOT NULL,\n quantity_sold INT NOT NULL,\n revenue DECIMAL(10,2) NOT NULL,\n FOREIGN KEY (product_id) REFERENCES products(product_id)\n) ENGINE=InnoDB;\n\nINSERT INTO products (product_name, category, price, stock_quantity) VALUES\n('Laptop Pro', 'Electronics', 1299.99, 50),\n('Wireless Mouse', 'Electronics', 29.99, 200),\n('Office Chair', 'Furniture', 249.99, 75),\n('Standing Desk', 'Furniture', 599.99, 30),\n('USB Hub', 'Electronics', 39.99, 150);\n\nINSERT INTO sales (product_id, sale_date, quantity_sold, revenue) VALUES\n(1, '2025-01-15', 10, 12999.90),\n(2, '2025-01-15', 50, 1499.50),\n(1, '2025-02-10', 8, 10399.92),\n(3, '2025-02-15', 20, 4999.80),\n(4, '2025-02-20', 5, 2999.95),\n(2, '2025-03-01', 100, 2999.00),\n(5, '2025-03-05', 75, 2999.25);\n\n<|Natural Language Question|>\nFor each product category, find the top-selling product (by total quantity sold) and show the category, product name, and total units sold.\n\n<|Start of SQL Query A|>\nWITH category_sales AS (\n SELECT\n p.category,\n p.product_id,\n p.product_name,\n SUM(s.quantity_sold) AS total_sold,\n ROW_NUMBER() OVER (PARTITION BY p.category ORDER BY SUM(s.quantity_sold) DESC) AS rn\n FROM products p\n JOIN sales s ON p.product_id = s.product_id\n GROUP BY p.category, p.product_id, p.product_name\n)\nSELECT category, product_name, total_sold\nFROM category_sales\nWHERE rn = 1;\n<|End of SQL Query A|>\n\n<|Start of SQL Query B|>\nSELECT \n p.category,\n p.product_name,\n t.total_units_sold\nFROM (\n SELECT \n p.category,\n p.product_id,\n SUM(s.quantity_sold) AS total_units_sold,\n RANK() OVER (PARTITION BY p.category ORDER BY SUM(s.quantity_sold) DESC) AS rnk\n FROM products p\n JOIN sales s ON p.product_id = s.product_id\n GROUP BY p.category, p.product_id\n) t\nJOIN products p ON p.product_id = t.product_id\nWHERE t.rnk = 1;\n<|End of SQL Query B|>", "role": "user", "type": "message"}], "instructions": null, "max_output_tokens": null, "max_tool_calls": null, "metadata": null, "model": null, "parallel_tool_calls": true, "previous_response_id": null, "prompt": null, "reasoning": null, "service_tier": null, "store": null, "temperature": null, "text": null, "tool_choice": "auto", "tools": [], "top_logprobs": null, "top_p": null, "truncation": null, "user": null, "stream": null}, "response": {"id": "resp_0a66fe4863e4a6c5006985206dc26c819183cb00fa6c46ddf5", "created_at": 1770332269.0, "error": null, "incomplete_details": null, "instructions": null, "metadata": {}, "model": "gpt-4.1-2025-04-14", "object": "response", "output": [{"id": "msg_0a66fe4863e4a6c5006985206e6a1881919fc34726abe61e21", "content": [{"annotations": [], "text": "Let's analyze both queries step by step for equivalence.\n\n---\n\n## Query A Breakdown\n\n```sql\nWITH category_sales AS (\n SELECT\n p.category,\n p.product_id,\n p.product_name,\n SUM(s.quantity_sold) AS total_sold,\n ROW_NUMBER() OVER (PARTITION BY p.category ORDER BY SUM(s.quantity_sold) DESC) AS rn\n FROM products p\n JOIN sales s ON p.product_id = s.product_id\n GROUP BY p.category, p.product_id, p.product_name\n)\nSELECT category, product_name, total_sold\nFROM category_sales\nWHERE rn = 1;\n```\n\n- Groups sales data by category & product, summing units sold.\n- For each (category, product), computes total sold, and assigns `ROW_NUMBER()` (partitioned by category, descending by sum).\n- Selects only row(s) with `rn = 1` per category (the **top-selling** product by quantity in each category).\n- Outputs: category, product_name, total_sold.\n\n---\n\n## Query B Breakdown\n\n```sql\nSELECT \n p.category,\n p.product_name,\n t.total_units_sold\nFROM (\n SELECT \n p.category,\n p.product_id,\n SUM(s.quantity_sold) AS total_units_sold,\n RANK() OVER (PARTITION BY p.category ORDER BY SUM(s.quantity_sold) DESC) AS rnk\n FROM products p\n JOIN sales s ON p.product_id = s.product_id\n GROUP BY p.category, p.product_id\n) t\nJOIN products p ON p.product_id = t.product_id\nWHERE t.rnk = 1;\n```\n\n- Inner subquery: For each (category, product), computes total_units_sold and assigns `RANK()` (partitioned by category, ordered by total_units_sold descending).\n- Outer query joins to `products` table again, returning category, product_name, and units sold for `rnk=1` per category (i.e., the highest sellers; multiple ties possible).\n- Outputs: category, product_name, total_units_sold.\n\n---\n\n## Key Points of Comparison\n\n### (1) **ROW_NUMBER() vs RANK()**\n\n- `ROW_NUMBER()` assigns a unique sequential number to each row within partition; in the event of ties (same total sold), only _one_ gets `rn=1`.\n- `RANK()` assigns the same rank to tied rows; multiple with same `total_units_sold` may all get rank 1.\n- Therefore: **If, in a category, two or more products tie for top sales, Query A will pick exactly one (arbitrary), Query B will pick all tied products.**\n\n---\n\n### (2) **Columns and Grouping**\n\n- Query A and Query B both group by category & product, sum up sales, use window function for \"top\" in category.\n- Query A groups by (category, product_id, product_name) while Query B groups by (category, product_id). This is a semantic no-op if product_id uniquely determines product_name (which is true per schema).\n- Both output same columns: category, product_name, total sold/units sold.\n\n---\n\n### (3) **Joins**\n\n- Query B includes an extra join to products to get product_name (since inner query does not select product_name).\n- This doesn't change output\u2014the mapping from product_id to product_name is 1:1.\n\n---\n\n## **Scenario Walkthrough**\n\nLet's see what happens for a tie.\n\nSuppose for category \"Electronics\" we have:\n\n| product_id | product_name | total_units_sold |\n|------------|---------------|------------------|\n| 2 | Wireless Mouse| 150 |\n| 5 | USB Hub | 150 |\n\n- Query A will output **only one** (row_number arbitrarily picks one).\n- Query B will output **both** (rank=1 for both tied records).\n\n---\n\n## **Verdict**\n\n**These queries are NOT functionally equivalent** because Query A returns one top-selling product per category even if there are ties, while Query B returns *all* top-selling products sharing the maximum sales in a category.\n\n**After analyzing both queries, my verdict is [[A!=B]]**.", "type": "output_text", "logprobs": []}], "role": "assistant", "status": "completed", "type": "message"}], "parallel_tool_calls": true, "temperature": 1.0, "tool_choice": "auto", "tools": [], "top_p": 1.0, "background": false, "conversation": null, "max_output_tokens": null, "max_tool_calls": null, "previous_response_id": null, "prompt": null, "prompt_cache_key": null, "reasoning": {"effort": null, "generate_summary": null, "summary": null}, "safety_identifier": null, "service_tier": "default", "status": "completed", "text": {"format": {"type": "text"}, "verbosity": "medium"}, "top_logprobs": 0, "truncation": "disabled", "usage": {"input_tokens": 1581, "input_tokens_details": {"cached_tokens": 0}, "output_tokens": 885, "output_tokens_details": {"reasoning_tokens": 0}, "total_tokens": 2466}, "user": null, "billing": {"payer": "developer"}, "completed_at": 1770332286, "frequency_penalty": 0.0, "presence_penalty": 0.0, "prompt_cache_retention": null, "store": true}, "verdict_label": "[[A!=B]]"}], "metadata": null} +{"responses_create_params": {"background": null, "include": null, "input": [{"content": "You are a SQL assistant who expertly maps natural language commands and problems into clean, efficient, and well-structured SQL queries. You always carefully examine the database context to include only the relevant tables and fields. You return only SQL code (no explanations outside of SQL code block; only proper comments are permitted). Wrap your SQL query inside a ```sql``` code block.", "role": "system", "type": "message"}, {"content": "sqlite\n\n\nCREATE TABLE employees (\n employee_id INTEGER PRIMARY KEY AUTOINCREMENT,\n first_name TEXT NOT NULL,\n last_name TEXT NOT NULL,\n department TEXT NOT NULL,\n hire_date TEXT NOT NULL,\n salary REAL NOT NULL,\n manager_id INTEGER REFERENCES employees(employee_id)\n);\n\nCREATE TABLE projects (\n project_id INTEGER PRIMARY KEY AUTOINCREMENT,\n project_name TEXT NOT NULL,\n start_date TEXT NOT NULL,\n end_date TEXT,\n budget REAL NOT NULL\n);\n\nCREATE TABLE project_assignments (\n assignment_id INTEGER PRIMARY KEY AUTOINCREMENT,\n employee_id INTEGER REFERENCES employees(employee_id),\n project_id INTEGER REFERENCES projects(project_id),\n role TEXT NOT NULL,\n hours_allocated INTEGER NOT NULL\n);\n\nINSERT INTO employees (first_name, last_name, department, hire_date, salary) VALUES\n('John', 'Doe', 'Engineering', '2020-01-15', 85000),\n('Jane', 'Smith', 'Engineering', '2019-06-20', 92000),\n('Bob', 'Johnson', 'Sales', '2021-03-10', 75000);\n\nINSERT INTO projects (project_name, start_date, end_date, budget) VALUES\n('Project Alpha', '2025-01-01', '2025-06-30', 500000),\n('Project Beta', '2025-02-15', NULL, 750000),\n('Project Gamma', '2025-03-01', '2025-12-31', 300000);\n\nINSERT INTO project_assignments (employee_id, project_id, role, hours_allocated) VALUES\n(1, 1, 'Developer', 160),\n(1, 2, 'Lead Developer', 200),\n(2, 1, 'Architect', 120),\n(2, 2, 'Developer', 180),\n(2, 3, 'Consultant', 40);\n\n\n\nList all employees who are assigned to more than one project, showing their full name, department, and the number of projects they're working on.", "role": "user", "type": "message"}], "instructions": null, "max_output_tokens": null, "max_tool_calls": null, "metadata": null, "model": null, "parallel_tool_calls": true, "previous_response_id": null, "prompt": null, "reasoning": null, "service_tier": null, "store": null, "temperature": null, "text": null, "tool_choice": "auto", "tools": [], "top_logprobs": null, "top_p": null, "truncation": null, "user": null, "stream": null}, "response": {"id": "resp_0183bfe0d1db1fc9006985206b4df8819e8591ba88be457806", "created_at": 1770332267.0, "error": null, "incomplete_details": null, "instructions": null, "metadata": {}, "model": "gpt-4.1-2025-04-14", "object": "response", "output": [{"id": "msg_0183bfe0d1db1fc9006985206bb47c819e850d5250cb16e7a0", "content": [{"annotations": [], "text": "```sql\nSELECT \n e.first_name || ' ' || e.last_name AS full_name,\n e.department,\n COUNT(DISTINCT pa.project_id) AS project_count\nFROM \n employees e\nJOIN \n project_assignments pa ON e.employee_id = pa.employee_id\nGROUP BY \n e.employee_id\nHAVING \n COUNT(DISTINCT pa.project_id) > 1;\n```", "type": "output_text", "logprobs": []}], "role": "assistant", "status": "completed", "type": "message"}], "parallel_tool_calls": true, "temperature": 1.0, "tool_choice": "auto", "tools": [], "top_p": 1.0, "background": false, "conversation": null, "max_output_tokens": null, "max_tool_calls": null, "previous_response_id": null, "prompt": null, "prompt_cache_key": null, "reasoning": {"effort": null, "generate_summary": null, "summary": null}, "safety_identifier": null, "service_tier": "default", "status": "completed", "text": {"format": {"type": "text"}, "verbosity": "medium"}, "top_logprobs": 0, "truncation": "disabled", "usage": {"input_tokens": 528, "input_tokens_details": {"cached_tokens": 0}, "output_tokens": 85, "output_tokens_details": {"reasoning_tokens": 0}, "total_tokens": 613}, "user": null, "billing": {"payer": "developer"}, "completed_at": 1770332269, "frequency_penalty": 0.0, "presence_penalty": 0.0, "prompt_cache_retention": null, "store": true}, "reward": 1.0, "uuid": "hr-projects-001", "sql": "SELECT\n e.first_name || ' ' || e.last_name AS full_name,\n e.department,\n COUNT(DISTINCT pa.project_id) AS project_count\nFROM employees e\nJOIN project_assignments pa ON e.employee_id = pa.employee_id\nGROUP BY e.employee_id, e.first_name, e.last_name, e.department\nHAVING COUNT(DISTINCT pa.project_id) > 1;", "model_output": "```sql\nSELECT \n e.first_name || ' ' || e.last_name AS full_name,\n e.department,\n COUNT(DISTINCT pa.project_id) AS project_count\nFROM \n employees e\nJOIN \n project_assignments pa ON e.employee_id = pa.employee_id\nGROUP BY \n e.employee_id\nHAVING \n COUNT(DISTINCT pa.project_id) > 1;\n```", "extracted_sql": "SELECT \n e.first_name || ' ' || e.last_name AS full_name,\n e.department,\n COUNT(DISTINCT pa.project_id) AS project_count\nFROM \n employees e\nJOIN \n project_assignments pa ON e.employee_id = pa.employee_id\nGROUP BY \n e.employee_id\nHAVING \n COUNT(DISTINCT pa.project_id) > 1;", "sql_dialect": "sqlite", "sql_context": "CREATE TABLE employees (\n employee_id INTEGER PRIMARY KEY AUTOINCREMENT,\n first_name TEXT NOT NULL,\n last_name TEXT NOT NULL,\n department TEXT NOT NULL,\n hire_date TEXT NOT NULL,\n salary REAL NOT NULL,\n manager_id INTEGER REFERENCES employees(employee_id)\n);\n\nCREATE TABLE projects (\n project_id INTEGER PRIMARY KEY AUTOINCREMENT,\n project_name TEXT NOT NULL,\n start_date TEXT NOT NULL,\n end_date TEXT,\n budget REAL NOT NULL\n);\n\nCREATE TABLE project_assignments (\n assignment_id INTEGER PRIMARY KEY AUTOINCREMENT,\n employee_id INTEGER REFERENCES employees(employee_id),\n project_id INTEGER REFERENCES projects(project_id),\n role TEXT NOT NULL,\n hours_allocated INTEGER NOT NULL\n);\n\nINSERT INTO employees (first_name, last_name, department, hire_date, salary) VALUES\n('John', 'Doe', 'Engineering', '2020-01-15', 85000),\n('Jane', 'Smith', 'Engineering', '2019-06-20', 92000),\n('Bob', 'Johnson', 'Sales', '2021-03-10', 75000);\n\nINSERT INTO projects (project_name, start_date, end_date, budget) VALUES\n('Project Alpha', '2025-01-01', '2025-06-30', 500000),\n('Project Beta', '2025-02-15', NULL, 750000),\n('Project Gamma', '2025-03-01', '2025-12-31', 300000);\n\nINSERT INTO project_assignments (employee_id, project_id, role, hours_allocated) VALUES\n(1, 1, 'Developer', 160),\n(1, 2, 'Lead Developer', 200),\n(2, 1, 'Architect', 120),\n(2, 2, 'Developer', 180),\n(2, 3, 'Consultant', 40);", "sql_prompt": "List all employees who are assigned to more than one project, showing their full name, department, and the number of projects they're working on.", "judge_passed": true, "failure_reason": "none", "judge_evaluations": [{"responses_create_params": {"background": null, "include": null, "input": [{"content": "You are an expert SQL judge evaluating the functional equivalence of two SQL queries. You will be given SQL query A and SQL query B. Your task is to determine whether both queries would produce identical result sets when executed against the provided database schema.\n\n## Core Evaluation Principle\n\nTwo SQL queries are functionally equivalent if and only if they return the same rows with the same column values for all possible database states conforming to the schema. Focus on semantic equivalence, not syntactic similarity.\n\n## SQL Equivalence Rules\n\n### Always Equivalent (ignore these differences):\n- **Formatting**: Whitespace, line breaks, capitalization of keywords\n- **Aliasing**: Different table/column aliases (e.g., `e.name` vs `employees.name`)\n- **JOIN syntax**: `FROM a, b WHERE a.id = b.id` \u2261 `FROM a JOIN b ON a.id = b.id`\n- **JOIN keyword**: `JOIN` \u2261 `INNER JOIN`\n- **Explicit vs implicit column lists**: When results are identical\n- **Redundant DISTINCT**: When the query already guarantees uniqueness (e.g., selecting a PRIMARY KEY)\n- **Parentheses**: Extra parentheses that don't change evaluation order\n- **Semicolons**: Trailing semicolon presence/absence\n\n### Equivalent Patterns (require careful analysis):\n- **CTE vs Subquery**: `WITH t AS (SELECT ...) SELECT * FROM t` may equal a nested subquery\n- **EXISTS vs IN vs JOIN**: Often equivalent for the same filtering logic\n- **COALESCE vs CASE**: `COALESCE(a, b)` \u2261 `CASE WHEN a IS NOT NULL THEN a ELSE b END`\n- **Boolean expressions**: `NOT (a AND b)` \u2261 `(NOT a) OR (NOT b)` (De Morgan's laws)\n- **Arithmetic**: `price * 1.1` \u2261 `price + price * 0.1`\n- **Date arithmetic**: Dialect-specific but logically equivalent date calculations\n\n### Never Equivalent (these always matter):\n- **Different columns selected**: `SELECT a, b` \u2260 `SELECT a, c`\n- **Different filtering**: `WHERE x > 5` \u2260 `WHERE x >= 5`\n- **Different aggregations**: `SUM(x)` \u2260 `AVG(x)`\n- **Different grouping**: `GROUP BY a` \u2260 `GROUP BY a, b`\n- **Missing/extra DISTINCT**: When it changes the result set\n- **Different ORDER BY**: When ordering is semantically required by the question\n- **Different LIMIT/OFFSET**: When they affect the result set\n- **NULL handling differences**: When they produce different results\n\n## Dialect-Specific Considerations\n\n### MySQL\n- Backtick quoting for identifiers: `table_name`.`column_name`\n- LIMIT syntax: `LIMIT n` or `LIMIT offset, count`\n- String functions: `SUBSTRING_INDEX()`, `CONCAT()`, `GROUP_CONCAT()`\n- Auto-increment: `AUTO_INCREMENT`\n- Boolean: `TINYINT(1)`, `TRUE`/`FALSE`\n\n### PostgreSQL\n- Double-quote identifiers: \"column\"\n- Type casting: `value::type` or `CAST(value AS type)`\n- Array operations: `ANY()`, `ALL()`, array literals\n- String functions: `SPLIT_PART()`, `STRING_AGG()`\n- Sequences: `SERIAL`, `BIGSERIAL`\n- Boolean: Native `BOOLEAN` type\n\n### SQLite\n- Limited type system (TEXT, INTEGER, REAL, BLOB)\n- String concatenation: `||` operator\n- No native BOOLEAN (uses 0/1)\n- `AUTOINCREMENT` keyword\n- No RIGHT JOIN or FULL OUTER JOIN\n\n## Output Format\n\nAnalyze both queries step by step, then provide your verdict:\n- If the queries are functionally equivalent: [[A=B]]\n- If the queries produce different results: [[A!=B]]\n\nExample: \"After analyzing both queries, my verdict is [[A=B]]\".", "role": "system", "type": "message"}, {"content": "<|SQL Dialect|>\nsqlite\n\n<|Database Schema and Sample Data|>\nCREATE TABLE employees (\n employee_id INTEGER PRIMARY KEY AUTOINCREMENT,\n first_name TEXT NOT NULL,\n last_name TEXT NOT NULL,\n department TEXT NOT NULL,\n hire_date TEXT NOT NULL,\n salary REAL NOT NULL,\n manager_id INTEGER REFERENCES employees(employee_id)\n);\n\nCREATE TABLE projects (\n project_id INTEGER PRIMARY KEY AUTOINCREMENT,\n project_name TEXT NOT NULL,\n start_date TEXT NOT NULL,\n end_date TEXT,\n budget REAL NOT NULL\n);\n\nCREATE TABLE project_assignments (\n assignment_id INTEGER PRIMARY KEY AUTOINCREMENT,\n employee_id INTEGER REFERENCES employees(employee_id),\n project_id INTEGER REFERENCES projects(project_id),\n role TEXT NOT NULL,\n hours_allocated INTEGER NOT NULL\n);\n\nINSERT INTO employees (first_name, last_name, department, hire_date, salary) VALUES\n('John', 'Doe', 'Engineering', '2020-01-15', 85000),\n('Jane', 'Smith', 'Engineering', '2019-06-20', 92000),\n('Bob', 'Johnson', 'Sales', '2021-03-10', 75000);\n\nINSERT INTO projects (project_name, start_date, end_date, budget) VALUES\n('Project Alpha', '2025-01-01', '2025-06-30', 500000),\n('Project Beta', '2025-02-15', NULL, 750000),\n('Project Gamma', '2025-03-01', '2025-12-31', 300000);\n\nINSERT INTO project_assignments (employee_id, project_id, role, hours_allocated) VALUES\n(1, 1, 'Developer', 160),\n(1, 2, 'Lead Developer', 200),\n(2, 1, 'Architect', 120),\n(2, 2, 'Developer', 180),\n(2, 3, 'Consultant', 40);\n\n<|Natural Language Question|>\nList all employees who are assigned to more than one project, showing their full name, department, and the number of projects they're working on.\n\n<|Start of SQL Query A|>\nSELECT\n e.first_name || ' ' || e.last_name AS full_name,\n e.department,\n COUNT(DISTINCT pa.project_id) AS project_count\nFROM employees e\nJOIN project_assignments pa ON e.employee_id = pa.employee_id\nGROUP BY e.employee_id, e.first_name, e.last_name, e.department\nHAVING COUNT(DISTINCT pa.project_id) > 1;\n<|End of SQL Query A|>\n\n<|Start of SQL Query B|>\nSELECT \n e.first_name || ' ' || e.last_name AS full_name,\n e.department,\n COUNT(DISTINCT pa.project_id) AS project_count\nFROM \n employees e\nJOIN \n project_assignments pa ON e.employee_id = pa.employee_id\nGROUP BY \n e.employee_id\nHAVING \n COUNT(DISTINCT pa.project_id) > 1;\n<|End of SQL Query B|>", "role": "user", "type": "message"}], "instructions": null, "max_output_tokens": null, "max_tool_calls": null, "metadata": null, "model": null, "parallel_tool_calls": true, "previous_response_id": null, "prompt": null, "reasoning": null, "service_tier": null, "store": null, "temperature": null, "text": null, "tool_choice": "auto", "tools": [], "top_logprobs": null, "top_p": null, "truncation": null, "user": null, "stream": null}, "response": {"id": "resp_0d610d54674f81fc006985206d795081a1841aa4304aab44de", "created_at": 1770332269.0, "error": null, "incomplete_details": null, "instructions": null, "metadata": {}, "model": "gpt-4.1-2025-04-14", "object": "response", "output": [{"id": "msg_0d610d54674f81fc006985206e770081a1b30434e357332898", "content": [{"annotations": [], "text": "Let's analyze both queries step by step, focusing on their SELECT, FROM/JOIN, GROUP BY, and HAVING clauses to determine if they are functionally equivalent for all possible conforming database states.\n\n---\n\n### SQL Query A\n\n```sql\nSELECT\n e.first_name || ' ' || e.last_name AS full_name,\n e.department,\n COUNT(DISTINCT pa.project_id) AS project_count\nFROM employees e\nJOIN project_assignments pa ON e.employee_id = pa.employee_id\nGROUP BY e.employee_id, e.first_name, e.last_name, e.department\nHAVING COUNT(DISTINCT pa.project_id) > 1;\n```\n\n- **SELECT**: Returns the full name (concatenated), department, and count of distinct project assignments for each employee.\n- **FROM/JOIN**: Joins `employees` to `project_assignments` via `employee_id`.\n- **GROUP BY**: Groups by `e.employee_id`, `e.first_name`, `e.last_name`, and `e.department`.\n- **HAVING**: Filters to only employees with more than one assigned project.\n\n---\n\n### SQL Query B\n\n```sql\nSELECT \n e.first_name || ' ' || e.last_name AS full_name,\n e.department,\n COUNT(DISTINCT pa.project_id) AS project_count\nFROM \n employees e\nJOIN \n project_assignments pa ON e.employee_id = pa.employee_id\nGROUP BY \n e.employee_id\nHAVING \n COUNT(DISTINCT pa.project_id) > 1;\n```\n\n- **SELECT**: Identical to Query A.\n- **FROM/JOIN**: Identical to Query A.\n- **GROUP BY**: Only groups by `e.employee_id`.\n- **HAVING**: Same as Query A.\n\n---\n\n### Key analysis: GROUP BY\n\n#### SQLite GROUP BY semantics:\n\n- In SQLite, when you group by a subset of the projected columns (e.g., only by primary key `e.employee_id`), the values of the non-aggregated, non-grouped columns in the SELECT list are **taken from an arbitrary row in the group** (since SQLite follows relaxed GROUP BY rules).\n- However, in this specific schema, `first_name`, `last_name`, and `department` are functionally dependent on `employee_id`, because `employee_id` is the primary key in `employees`. That is, for each `employee_id`, there is exactly one row (and values) for those columns.\n\nThus: \n- `GROUP BY e.employee_id, e.first_name, e.last_name, e.department` is **functionally equivalent** to `GROUP BY e.employee_id` in this schema because the additional columns are fully determined by `employee_id`. No possible state can have two rows with the same employee_id but different names or departments.\n\n#### Remaining clauses\n\n- Both queries count distinct projects, in the same way, and apply the same HAVING filter.\n- Both SELECT the same expressions, so the output columns and values will always match for each matching employee.\n\n### Conclusion\n\n**For this schema, both queries by logical necessity must return identical results for all database states, because the denormalization introduced by the extra GROUP BY columns has no effect (there's only one value per employee_id for first_name, last_name, department).**\n\n**If the schema were not normalized or employee_id was not unique, there could be a difference, but in this case, the schema ensures equivalence.**\n\n---\n\nAfter analyzing both queries, my verdict is **[[A=B]]**.", "type": "output_text", "logprobs": []}], "role": "assistant", "status": "completed", "type": "message"}], "parallel_tool_calls": true, "temperature": 1.0, "tool_choice": "auto", "tools": [], "top_p": 1.0, "background": false, "conversation": null, "max_output_tokens": null, "max_tool_calls": null, "previous_response_id": null, "prompt": null, "prompt_cache_key": null, "reasoning": {"effort": null, "generate_summary": null, "summary": null}, "safety_identifier": null, "service_tier": "default", "status": "completed", "text": {"format": {"type": "text"}, "verbosity": "medium"}, "top_logprobs": 0, "truncation": "disabled", "usage": {"input_tokens": 1509, "input_tokens_details": {"cached_tokens": 0}, "output_tokens": 730, "output_tokens_details": {"reasoning_tokens": 0}, "total_tokens": 2239}, "user": null, "billing": {"payer": "developer"}, "completed_at": 1770332282, "frequency_penalty": 0.0, "presence_penalty": 0.0, "prompt_cache_retention": null, "store": true}, "verdict_label": "[[A=B]]"}, {"responses_create_params": {"background": null, "include": null, "input": [{"content": "You are an expert SQL judge evaluating the functional equivalence of two SQL queries. You will be given SQL query A and SQL query B. Your task is to determine whether both queries would produce identical result sets when executed against the provided database schema.\n\n## Core Evaluation Principle\n\nTwo SQL queries are functionally equivalent if and only if they return the same rows with the same column values for all possible database states conforming to the schema. Focus on semantic equivalence, not syntactic similarity.\n\n## SQL Equivalence Rules\n\n### Always Equivalent (ignore these differences):\n- **Formatting**: Whitespace, line breaks, capitalization of keywords\n- **Aliasing**: Different table/column aliases (e.g., `e.name` vs `employees.name`)\n- **JOIN syntax**: `FROM a, b WHERE a.id = b.id` \u2261 `FROM a JOIN b ON a.id = b.id`\n- **JOIN keyword**: `JOIN` \u2261 `INNER JOIN`\n- **Explicit vs implicit column lists**: When results are identical\n- **Redundant DISTINCT**: When the query already guarantees uniqueness (e.g., selecting a PRIMARY KEY)\n- **Parentheses**: Extra parentheses that don't change evaluation order\n- **Semicolons**: Trailing semicolon presence/absence\n\n### Equivalent Patterns (require careful analysis):\n- **CTE vs Subquery**: `WITH t AS (SELECT ...) SELECT * FROM t` may equal a nested subquery\n- **EXISTS vs IN vs JOIN**: Often equivalent for the same filtering logic\n- **COALESCE vs CASE**: `COALESCE(a, b)` \u2261 `CASE WHEN a IS NOT NULL THEN a ELSE b END`\n- **Boolean expressions**: `NOT (a AND b)` \u2261 `(NOT a) OR (NOT b)` (De Morgan's laws)\n- **Arithmetic**: `price * 1.1` \u2261 `price + price * 0.1`\n- **Date arithmetic**: Dialect-specific but logically equivalent date calculations\n\n### Never Equivalent (these always matter):\n- **Different columns selected**: `SELECT a, b` \u2260 `SELECT a, c`\n- **Different filtering**: `WHERE x > 5` \u2260 `WHERE x >= 5`\n- **Different aggregations**: `SUM(x)` \u2260 `AVG(x)`\n- **Different grouping**: `GROUP BY a` \u2260 `GROUP BY a, b`\n- **Missing/extra DISTINCT**: When it changes the result set\n- **Different ORDER BY**: When ordering is semantically required by the question\n- **Different LIMIT/OFFSET**: When they affect the result set\n- **NULL handling differences**: When they produce different results\n\n## Dialect-Specific Considerations\n\n### MySQL\n- Backtick quoting for identifiers: `table_name`.`column_name`\n- LIMIT syntax: `LIMIT n` or `LIMIT offset, count`\n- String functions: `SUBSTRING_INDEX()`, `CONCAT()`, `GROUP_CONCAT()`\n- Auto-increment: `AUTO_INCREMENT`\n- Boolean: `TINYINT(1)`, `TRUE`/`FALSE`\n\n### PostgreSQL\n- Double-quote identifiers: \"column\"\n- Type casting: `value::type` or `CAST(value AS type)`\n- Array operations: `ANY()`, `ALL()`, array literals\n- String functions: `SPLIT_PART()`, `STRING_AGG()`\n- Sequences: `SERIAL`, `BIGSERIAL`\n- Boolean: Native `BOOLEAN` type\n\n### SQLite\n- Limited type system (TEXT, INTEGER, REAL, BLOB)\n- String concatenation: `||` operator\n- No native BOOLEAN (uses 0/1)\n- `AUTOINCREMENT` keyword\n- No RIGHT JOIN or FULL OUTER JOIN\n\n## Output Format\n\nAnalyze both queries step by step, then provide your verdict:\n- If the queries are functionally equivalent: [[A=B]]\n- If the queries produce different results: [[A!=B]]\n\nExample: \"After analyzing both queries, my verdict is [[A=B]]\".", "role": "system", "type": "message"}, {"content": "<|SQL Dialect|>\nsqlite\n\n<|Database Schema and Sample Data|>\nCREATE TABLE employees (\n employee_id INTEGER PRIMARY KEY AUTOINCREMENT,\n first_name TEXT NOT NULL,\n last_name TEXT NOT NULL,\n department TEXT NOT NULL,\n hire_date TEXT NOT NULL,\n salary REAL NOT NULL,\n manager_id INTEGER REFERENCES employees(employee_id)\n);\n\nCREATE TABLE projects (\n project_id INTEGER PRIMARY KEY AUTOINCREMENT,\n project_name TEXT NOT NULL,\n start_date TEXT NOT NULL,\n end_date TEXT,\n budget REAL NOT NULL\n);\n\nCREATE TABLE project_assignments (\n assignment_id INTEGER PRIMARY KEY AUTOINCREMENT,\n employee_id INTEGER REFERENCES employees(employee_id),\n project_id INTEGER REFERENCES projects(project_id),\n role TEXT NOT NULL,\n hours_allocated INTEGER NOT NULL\n);\n\nINSERT INTO employees (first_name, last_name, department, hire_date, salary) VALUES\n('John', 'Doe', 'Engineering', '2020-01-15', 85000),\n('Jane', 'Smith', 'Engineering', '2019-06-20', 92000),\n('Bob', 'Johnson', 'Sales', '2021-03-10', 75000);\n\nINSERT INTO projects (project_name, start_date, end_date, budget) VALUES\n('Project Alpha', '2025-01-01', '2025-06-30', 500000),\n('Project Beta', '2025-02-15', NULL, 750000),\n('Project Gamma', '2025-03-01', '2025-12-31', 300000);\n\nINSERT INTO project_assignments (employee_id, project_id, role, hours_allocated) VALUES\n(1, 1, 'Developer', 160),\n(1, 2, 'Lead Developer', 200),\n(2, 1, 'Architect', 120),\n(2, 2, 'Developer', 180),\n(2, 3, 'Consultant', 40);\n\n<|Natural Language Question|>\nList all employees who are assigned to more than one project, showing their full name, department, and the number of projects they're working on.\n\n<|Start of SQL Query A|>\nSELECT \n e.first_name || ' ' || e.last_name AS full_name,\n e.department,\n COUNT(DISTINCT pa.project_id) AS project_count\nFROM \n employees e\nJOIN \n project_assignments pa ON e.employee_id = pa.employee_id\nGROUP BY \n e.employee_id\nHAVING \n COUNT(DISTINCT pa.project_id) > 1;\n<|End of SQL Query A|>\n\n<|Start of SQL Query B|>\nSELECT\n e.first_name || ' ' || e.last_name AS full_name,\n e.department,\n COUNT(DISTINCT pa.project_id) AS project_count\nFROM employees e\nJOIN project_assignments pa ON e.employee_id = pa.employee_id\nGROUP BY e.employee_id, e.first_name, e.last_name, e.department\nHAVING COUNT(DISTINCT pa.project_id) > 1;\n<|End of SQL Query B|>", "role": "user", "type": "message"}], "instructions": null, "max_output_tokens": null, "max_tool_calls": null, "metadata": null, "model": null, "parallel_tool_calls": true, "previous_response_id": null, "prompt": null, "reasoning": null, "service_tier": null, "store": null, "temperature": null, "text": null, "tool_choice": "auto", "tools": [], "top_logprobs": null, "top_p": null, "truncation": null, "user": null, "stream": null}, "response": {"id": "resp_07c3c0a2f5e494e0006985207b66488192bc456f9f48ea297d", "created_at": 1770332283.0, "error": null, "incomplete_details": null, "instructions": null, "metadata": {}, "model": "gpt-4.1-2025-04-14", "object": "response", "output": [{"id": "msg_07c3c0a2f5e494e0006985207bd0448192b924910ea5e59281", "content": [{"annotations": [], "text": "Let's analyze both queries against the schema, the data, and the request:\n\n---\n\n### Restated Question\nList all employees who are assigned to **more than one project**, showing:\n- their **full name** (first_name + ' ' + last_name)\n- their **department**\n- the **number of projects** they're working on\n\n---\n\n## Query A\n\n```sql\nSELECT \n e.first_name || ' ' || e.last_name AS full_name,\n e.department,\n COUNT(DISTINCT pa.project_id) AS project_count\nFROM \n employees e\nJOIN \n project_assignments pa ON e.employee_id = pa.employee_id\nGROUP BY \n e.employee_id\nHAVING \n COUNT(DISTINCT pa.project_id) > 1;\n```\n\n### Logic:\n- Joins `employees` to `project_assignments` on `employee_id`\n- For each `employee_id`, groups all their project assignments\n- Counts **distinct** project assignments for each employee\n- Returns only those employees with `project_count > 1`\n- Selects full name, department, project count\n\n---\n\n## Query B\n\n```sql\nSELECT\n e.first_name || ' ' || e.last_name AS full_name,\n e.department,\n COUNT(DISTINCT pa.project_id) AS project_count\nFROM employees e\nJOIN project_assignments pa ON e.employee_id = pa.employee_id\nGROUP BY e.employee_id, e.first_name, e.last_name, e.department\nHAVING COUNT(DISTINCT pa.project_id) > 1;\n```\n\n### Logic:\n- The same logic and joins as A\n- The only difference is in the `GROUP BY` clause, which explicitly lists:\n - `e.employee_id`, `e.first_name`, `e.last_name`, `e.department`\n- Still selects the same columns and applies the same HAVING\n\n---\n\n## Analysis\n\n### GROUP BY Clause Differences\n\n- **Query A**: `GROUP BY e.employee_id`\n- **Query B**: `GROUP BY e.employee_id, e.first_name, e.last_name, e.department`\n\n#### In SQLite:\n- SQLite allows you to `GROUP BY` just the primary key (here, `e.employee_id`), and you can select any columns from the grouped row because `employee_id` is the PRIMARY KEY.\n- All other selected `e.*` columns for a grouped `employee_id` will always match the values for that employee.\n\n- Listing all the other columns (`first_name`, `last_name`, `department`) in the `GROUP BY` in Query B is redundant *when* `employee_id` is unique for each employee (which it is, as the PK), but produces the same groups.\n\n### SELECT Columns\n- Both select:\n - Full name (`first_name` || ' ' || `last_name`)\n - Department\n - Project count\n\n### HAVING Clause\n- Both filter for `project_count > 1` in the exact same way.\n\n### DISTINCT\n- Both use `COUNT(DISTINCT pa.project_id)`\n\n### Data Output\n- No difference in output for any possible database conforming to this schema, because for a given `employee_id`, the other grouped columns (`first_name`, `last_name`, `department`) are functionally dependent and always identical, due to the primary key.\n\n---\n\n## Verdict\n\nAfter analyzing both queries, my verdict is [[A=B]].", "type": "output_text", "logprobs": []}], "role": "assistant", "status": "completed", "type": "message"}], "parallel_tool_calls": true, "temperature": 1.0, "tool_choice": "auto", "tools": [], "top_p": 1.0, "background": false, "conversation": null, "max_output_tokens": null, "max_tool_calls": null, "previous_response_id": null, "prompt": null, "prompt_cache_key": null, "reasoning": {"effort": null, "generate_summary": null, "summary": null}, "safety_identifier": null, "service_tier": "default", "status": "completed", "text": {"format": {"type": "text"}, "verbosity": "medium"}, "top_logprobs": 0, "truncation": "disabled", "usage": {"input_tokens": 1509, "input_tokens_details": {"cached_tokens": 0}, "output_tokens": 699, "output_tokens_details": {"reasoning_tokens": 0}, "total_tokens": 2208}, "user": null, "billing": {"payer": "developer"}, "completed_at": 1770332296, "frequency_penalty": 0.0, "presence_penalty": 0.0, "prompt_cache_retention": null, "store": true}, "verdict_label": "[[A=B]]"}], "metadata": null} +{"responses_create_params": {"background": null, "include": null, "input": [{"content": "You are a SQL assistant who expertly maps natural language commands and problems into clean, efficient, and well-structured SQL queries. You always carefully examine the database context to include only the relevant tables and fields. You return only SQL code (no explanations outside of SQL code block; only proper comments are permitted). Wrap your SQL query inside a ```sql``` code block.", "role": "system", "type": "message"}, {"content": "mysql\n\n\nCREATE TABLE weather_station (\n station_id INT UNSIGNED AUTO_INCREMENT PRIMARY KEY,\n station_name VARCHAR(100) NOT NULL,\n latitude DECIMAL(9,6) NOT NULL,\n longitude DECIMAL(9,6) NOT NULL,\n installed_date DATE NOT NULL,\n created_at DATETIME NOT NULL,\n updated_at DATETIME NOT NULL,\n is_active TINYINT(1) NOT NULL DEFAULT 1,\n description TEXT\n) ENGINE=InnoDB;\n\nCREATE TABLE sensor (\n sensor_id INT UNSIGNED AUTO_INCREMENT PRIMARY KEY,\n station_id INT UNSIGNED NOT NULL,\n sensor_type VARCHAR(50) NOT NULL,\n calibration_date DATE NOT NULL,\n serial_number VARCHAR(50),\n firmware_version VARCHAR(20),\n is_active TINYINT(1) NOT NULL DEFAULT 1,\n created_at DATETIME NOT NULL,\n updated_at DATETIME NOT NULL,\n description TEXT,\n FOREIGN KEY (station_id) REFERENCES weather_station(station_id)\n) ENGINE=InnoDB;\n\nCREATE TABLE sensor_log (\n log_id BIGINT UNSIGNED AUTO_INCREMENT PRIMARY KEY,\n sensor_id INT UNSIGNED NOT NULL,\n drift_date DATE NOT NULL,\n temperature_deviation DECIMAL(5,2) NOT NULL,\n reporting_email VARCHAR(150) NOT NULL,\n log_url VARCHAR(255) NOT NULL,\n readings TEXT NOT NULL,\n created_at DATETIME NOT NULL,\n updated_at DATETIME NOT NULL,\n is_processed TINYINT(1) NOT NULL DEFAULT 0,\n notes TEXT,\n FOREIGN KEY (sensor_id) REFERENCES sensor(sensor_id)\n) ENGINE=InnoDB;\n\nINSERT INTO weather_station (station_name, latitude, longitude, installed_date, created_at, updated_at, is_active, description) VALUES\n('North Ridge', 45.123456, -122.654321, '2023-03-15', '2023-04-01 08:00:00', '2023-04-01 08:00:00', 1, 'High elevation site'),\n('East Valley', 44.987654, -122.112233, '2022-11-20', '2022-12-01 09:15:00', '2022-12-01 09:15:00', 1, NULL);\n\nINSERT INTO sensor (station_id, sensor_type, calibration_date, serial_number, firmware_version, is_active, created_at, updated_at, description) VALUES\n(1, 'Thermometer', '2025-01-01', 'SN-001', 'v1.2.0', 1, '2025-01-02 08:00:00', '2025-01-02 08:00:00', NULL),\n(2, 'Thermometer', '2024-12-10', 'SN-003', 'v1.1.3', 1, '2024-12-12 09:00:00', '2024-12-12 09:00:00', NULL);\n\nINSERT INTO sensor_log (sensor_id, drift_date, temperature_deviation, reporting_email, log_url, readings, created_at, updated_at, is_processed, notes) VALUES\n(1, '2025-07-01', 0.45, 'tech.north@example.com', 'http://logs.example.com/north/2025-07-01', '[21.2,21.5,21.7]', '2025-07-02 08:10:00', '2025-07-02 08:10:00', 1, NULL),\n(1, '2025-07-15', 0.30, 'ops.north@example.com', 'http://logs.example.com/north/2025-07-15', '[21.0,21.1]', '2025-07-16 08:12:00', '2025-07-16 08:12:00', 0, NULL),\n(2, '2025-07-03', -0.20, 'maintenance@eastvalley.org', 'http://logs.example.com/east/2025-07-03', '[19.8,20.0,20.2,20.3]', '2025-07-04 09:00:00', '2025-07-04 09:00:00', 1, NULL);\n\n\n\nPlease retrieve for each weather station the latest sensor-drift date, the average temperature deviation, and the domain extracted from the reporting email within the sensor log URL, omitting any records whose embedded readings array contains fewer than three entries, and return the list ordered by descending average deviation.", "role": "user", "type": "message"}], "instructions": null, "max_output_tokens": null, "max_tool_calls": null, "metadata": null, "model": null, "parallel_tool_calls": true, "previous_response_id": null, "prompt": null, "reasoning": null, "service_tier": null, "store": null, "temperature": null, "text": null, "tool_choice": "auto", "tools": [], "top_logprobs": null, "top_p": null, "truncation": null, "user": null, "stream": null}, "response": {"id": "resp_0d0c8a4b3ee890a8006985206b10dc81a298ec042d64097ddc", "created_at": 1770332267.0, "error": null, "incomplete_details": null, "instructions": null, "metadata": {}, "model": "gpt-4.1-2025-04-14", "object": "response", "output": [{"id": "msg_0d0c8a4b3ee890a8006985206baf7081a28e7ca4eb51790b94", "content": [{"annotations": [], "text": "```sql\nSELECT\n ws.station_name,\n MAX(sl.drift_date) AS latest_drift_date,\n AVG(sl.temperature_deviation) AS avg_temperature_deviation,\n SUBSTRING_INDEX(\n SUBSTRING_INDEX(sl.reporting_email, '@', -1),\n '.', -2\n ) AS email_domain\nFROM\n weather_station ws\nJOIN\n sensor s ON s.station_id = ws.station_id\nJOIN\n sensor_log sl ON sl.sensor_id = s.sensor_id\nWHERE\n -- Only consider logs whose readings array has 3 or more entries\n (\n LENGTH(sl.readings) - LENGTH(REPLACE(sl.readings, ',', ''))\n ) >= 2\nGROUP BY\n ws.station_id,\n email_domain\nORDER BY\n avg_temperature_deviation DESC;\n```", "type": "output_text", "logprobs": []}], "role": "assistant", "status": "completed", "type": "message"}], "parallel_tool_calls": true, "temperature": 1.0, "tool_choice": "auto", "tools": [], "top_p": 1.0, "background": false, "conversation": null, "max_output_tokens": null, "max_tool_calls": null, "previous_response_id": null, "prompt": null, "prompt_cache_key": null, "reasoning": {"effort": null, "generate_summary": null, "summary": null}, "safety_identifier": null, "service_tier": "default", "status": "completed", "text": {"format": {"type": "text"}, "verbosity": "medium"}, "top_logprobs": 0, "truncation": "disabled", "usage": {"input_tokens": 1093, "input_tokens_details": {"cached_tokens": 0}, "output_tokens": 167, "output_tokens_details": {"reasoning_tokens": 0}, "total_tokens": 1260}, "user": null, "billing": {"payer": "developer"}, "completed_at": 1770332269, "frequency_penalty": 0.0, "presence_penalty": 0.0, "prompt_cache_retention": null, "store": true}, "reward": 0.0, "uuid": "weather-station-001", "sql": "WITH filtered_logs AS (\n SELECT\n sl.log_id,\n s.station_id,\n ws.station_name,\n sl.drift_date,\n sl.temperature_deviation,\n SUBSTRING_INDEX(sl.reporting_email, '@', -1) AS email_domain\n FROM sensor_log sl\n JOIN sensor s ON sl.sensor_id = s.sensor_id\n JOIN weather_station ws ON s.station_id = ws.station_id\n WHERE (LENGTH(sl.readings) - LENGTH(REPLACE(sl.readings, ',', ''))) >= 2\n),\nagg AS (\n SELECT\n station_id,\n station_name,\n MAX(drift_date) AS latest_drift_date,\n AVG(temperature_deviation) AS avg_temp_deviation\n FROM filtered_logs\n GROUP BY station_id, station_name\n),\nlatest_domain AS (\n SELECT station_id, email_domain\n FROM (\n SELECT\n fl.station_id,\n fl.email_domain,\n ROW_NUMBER() OVER (PARTITION BY fl.station_id ORDER BY fl.drift_date DESC) AS rn\n FROM filtered_logs fl\n ) t\n WHERE rn = 1\n)\nSELECT\n a.station_id,\n a.station_name,\n a.latest_drift_date,\n ROUND(a.avg_temp_deviation, 2) AS avg_temp_deviation,\n d.email_domain\nFROM agg a\nJOIN latest_domain d ON a.station_id = d.station_id\nORDER BY a.avg_temp_deviation DESC;", "model_output": "```sql\nSELECT\n ws.station_name,\n MAX(sl.drift_date) AS latest_drift_date,\n AVG(sl.temperature_deviation) AS avg_temperature_deviation,\n SUBSTRING_INDEX(\n SUBSTRING_INDEX(sl.reporting_email, '@', -1),\n '.', -2\n ) AS email_domain\nFROM\n weather_station ws\nJOIN\n sensor s ON s.station_id = ws.station_id\nJOIN\n sensor_log sl ON sl.sensor_id = s.sensor_id\nWHERE\n -- Only consider logs whose readings array has 3 or more entries\n (\n LENGTH(sl.readings) - LENGTH(REPLACE(sl.readings, ',', ''))\n ) >= 2\nGROUP BY\n ws.station_id,\n email_domain\nORDER BY\n avg_temperature_deviation DESC;\n```", "extracted_sql": "SELECT\n ws.station_name,\n MAX(sl.drift_date) AS latest_drift_date,\n AVG(sl.temperature_deviation) AS avg_temperature_deviation,\n SUBSTRING_INDEX(\n SUBSTRING_INDEX(sl.reporting_email, '@', -1),\n '.', -2\n ) AS email_domain\nFROM\n weather_station ws\nJOIN\n sensor s ON s.station_id = ws.station_id\nJOIN\n sensor_log sl ON sl.sensor_id = s.sensor_id\nWHERE\n -- Only consider logs whose readings array has 3 or more entries\n (\n LENGTH(sl.readings) - LENGTH(REPLACE(sl.readings, ',', ''))\n ) >= 2\nGROUP BY\n ws.station_id,\n email_domain\nORDER BY\n avg_temperature_deviation DESC;", "sql_dialect": "mysql", "sql_context": "CREATE TABLE weather_station (\n station_id INT UNSIGNED AUTO_INCREMENT PRIMARY KEY,\n station_name VARCHAR(100) NOT NULL,\n latitude DECIMAL(9,6) NOT NULL,\n longitude DECIMAL(9,6) NOT NULL,\n installed_date DATE NOT NULL,\n created_at DATETIME NOT NULL,\n updated_at DATETIME NOT NULL,\n is_active TINYINT(1) NOT NULL DEFAULT 1,\n description TEXT\n) ENGINE=InnoDB;\n\nCREATE TABLE sensor (\n sensor_id INT UNSIGNED AUTO_INCREMENT PRIMARY KEY,\n station_id INT UNSIGNED NOT NULL,\n sensor_type VARCHAR(50) NOT NULL,\n calibration_date DATE NOT NULL,\n serial_number VARCHAR(50),\n firmware_version VARCHAR(20),\n is_active TINYINT(1) NOT NULL DEFAULT 1,\n created_at DATETIME NOT NULL,\n updated_at DATETIME NOT NULL,\n description TEXT,\n FOREIGN KEY (station_id) REFERENCES weather_station(station_id)\n) ENGINE=InnoDB;\n\nCREATE TABLE sensor_log (\n log_id BIGINT UNSIGNED AUTO_INCREMENT PRIMARY KEY,\n sensor_id INT UNSIGNED NOT NULL,\n drift_date DATE NOT NULL,\n temperature_deviation DECIMAL(5,2) NOT NULL,\n reporting_email VARCHAR(150) NOT NULL,\n log_url VARCHAR(255) NOT NULL,\n readings TEXT NOT NULL,\n created_at DATETIME NOT NULL,\n updated_at DATETIME NOT NULL,\n is_processed TINYINT(1) NOT NULL DEFAULT 0,\n notes TEXT,\n FOREIGN KEY (sensor_id) REFERENCES sensor(sensor_id)\n) ENGINE=InnoDB;\n\nINSERT INTO weather_station (station_name, latitude, longitude, installed_date, created_at, updated_at, is_active, description) VALUES\n('North Ridge', 45.123456, -122.654321, '2023-03-15', '2023-04-01 08:00:00', '2023-04-01 08:00:00', 1, 'High elevation site'),\n('East Valley', 44.987654, -122.112233, '2022-11-20', '2022-12-01 09:15:00', '2022-12-01 09:15:00', 1, NULL);\n\nINSERT INTO sensor (station_id, sensor_type, calibration_date, serial_number, firmware_version, is_active, created_at, updated_at, description) VALUES\n(1, 'Thermometer', '2025-01-01', 'SN-001', 'v1.2.0', 1, '2025-01-02 08:00:00', '2025-01-02 08:00:00', NULL),\n(2, 'Thermometer', '2024-12-10', 'SN-003', 'v1.1.3', 1, '2024-12-12 09:00:00', '2024-12-12 09:00:00', NULL);\n\nINSERT INTO sensor_log (sensor_id, drift_date, temperature_deviation, reporting_email, log_url, readings, created_at, updated_at, is_processed, notes) VALUES\n(1, '2025-07-01', 0.45, 'tech.north@example.com', 'http://logs.example.com/north/2025-07-01', '[21.2,21.5,21.7]', '2025-07-02 08:10:00', '2025-07-02 08:10:00', 1, NULL),\n(1, '2025-07-15', 0.30, 'ops.north@example.com', 'http://logs.example.com/north/2025-07-15', '[21.0,21.1]', '2025-07-16 08:12:00', '2025-07-16 08:12:00', 0, NULL),\n(2, '2025-07-03', -0.20, 'maintenance@eastvalley.org', 'http://logs.example.com/east/2025-07-03', '[19.8,20.0,20.2,20.3]', '2025-07-04 09:00:00', '2025-07-04 09:00:00', 1, NULL);", "sql_prompt": "Please retrieve for each weather station the latest sensor-drift date, the average temperature deviation, and the domain extracted from the reporting email within the sensor log URL, omitting any records whose embedded readings array contains fewer than three entries, and return the list ordered by descending average deviation.", "judge_passed": false, "failure_reason": "judge_evaluation_failed", "judge_evaluations": [{"responses_create_params": {"background": null, "include": null, "input": [{"content": "You are an expert SQL judge evaluating the functional equivalence of two SQL queries. You will be given SQL query A and SQL query B. Your task is to determine whether both queries would produce identical result sets when executed against the provided database schema.\n\n## Core Evaluation Principle\n\nTwo SQL queries are functionally equivalent if and only if they return the same rows with the same column values for all possible database states conforming to the schema. Focus on semantic equivalence, not syntactic similarity.\n\n## SQL Equivalence Rules\n\n### Always Equivalent (ignore these differences):\n- **Formatting**: Whitespace, line breaks, capitalization of keywords\n- **Aliasing**: Different table/column aliases (e.g., `e.name` vs `employees.name`)\n- **JOIN syntax**: `FROM a, b WHERE a.id = b.id` \u2261 `FROM a JOIN b ON a.id = b.id`\n- **JOIN keyword**: `JOIN` \u2261 `INNER JOIN`\n- **Explicit vs implicit column lists**: When results are identical\n- **Redundant DISTINCT**: When the query already guarantees uniqueness (e.g., selecting a PRIMARY KEY)\n- **Parentheses**: Extra parentheses that don't change evaluation order\n- **Semicolons**: Trailing semicolon presence/absence\n\n### Equivalent Patterns (require careful analysis):\n- **CTE vs Subquery**: `WITH t AS (SELECT ...) SELECT * FROM t` may equal a nested subquery\n- **EXISTS vs IN vs JOIN**: Often equivalent for the same filtering logic\n- **COALESCE vs CASE**: `COALESCE(a, b)` \u2261 `CASE WHEN a IS NOT NULL THEN a ELSE b END`\n- **Boolean expressions**: `NOT (a AND b)` \u2261 `(NOT a) OR (NOT b)` (De Morgan's laws)\n- **Arithmetic**: `price * 1.1` \u2261 `price + price * 0.1`\n- **Date arithmetic**: Dialect-specific but logically equivalent date calculations\n\n### Never Equivalent (these always matter):\n- **Different columns selected**: `SELECT a, b` \u2260 `SELECT a, c`\n- **Different filtering**: `WHERE x > 5` \u2260 `WHERE x >= 5`\n- **Different aggregations**: `SUM(x)` \u2260 `AVG(x)`\n- **Different grouping**: `GROUP BY a` \u2260 `GROUP BY a, b`\n- **Missing/extra DISTINCT**: When it changes the result set\n- **Different ORDER BY**: When ordering is semantically required by the question\n- **Different LIMIT/OFFSET**: When they affect the result set\n- **NULL handling differences**: When they produce different results\n\n## Dialect-Specific Considerations\n\n### MySQL\n- Backtick quoting for identifiers: `table_name`.`column_name`\n- LIMIT syntax: `LIMIT n` or `LIMIT offset, count`\n- String functions: `SUBSTRING_INDEX()`, `CONCAT()`, `GROUP_CONCAT()`\n- Auto-increment: `AUTO_INCREMENT`\n- Boolean: `TINYINT(1)`, `TRUE`/`FALSE`\n\n### PostgreSQL\n- Double-quote identifiers: \"column\"\n- Type casting: `value::type` or `CAST(value AS type)`\n- Array operations: `ANY()`, `ALL()`, array literals\n- String functions: `SPLIT_PART()`, `STRING_AGG()`\n- Sequences: `SERIAL`, `BIGSERIAL`\n- Boolean: Native `BOOLEAN` type\n\n### SQLite\n- Limited type system (TEXT, INTEGER, REAL, BLOB)\n- String concatenation: `||` operator\n- No native BOOLEAN (uses 0/1)\n- `AUTOINCREMENT` keyword\n- No RIGHT JOIN or FULL OUTER JOIN\n\n## Output Format\n\nAnalyze both queries step by step, then provide your verdict:\n- If the queries are functionally equivalent: [[A=B]]\n- If the queries produce different results: [[A!=B]]\n\nExample: \"After analyzing both queries, my verdict is [[A=B]]\".", "role": "system", "type": "message"}, {"content": "<|SQL Dialect|>\nmysql\n\n<|Database Schema and Sample Data|>\nCREATE TABLE weather_station (\n station_id INT UNSIGNED AUTO_INCREMENT PRIMARY KEY,\n station_name VARCHAR(100) NOT NULL,\n latitude DECIMAL(9,6) NOT NULL,\n longitude DECIMAL(9,6) NOT NULL,\n installed_date DATE NOT NULL,\n created_at DATETIME NOT NULL,\n updated_at DATETIME NOT NULL,\n is_active TINYINT(1) NOT NULL DEFAULT 1,\n description TEXT\n) ENGINE=InnoDB;\n\nCREATE TABLE sensor (\n sensor_id INT UNSIGNED AUTO_INCREMENT PRIMARY KEY,\n station_id INT UNSIGNED NOT NULL,\n sensor_type VARCHAR(50) NOT NULL,\n calibration_date DATE NOT NULL,\n serial_number VARCHAR(50),\n firmware_version VARCHAR(20),\n is_active TINYINT(1) NOT NULL DEFAULT 1,\n created_at DATETIME NOT NULL,\n updated_at DATETIME NOT NULL,\n description TEXT,\n FOREIGN KEY (station_id) REFERENCES weather_station(station_id)\n) ENGINE=InnoDB;\n\nCREATE TABLE sensor_log (\n log_id BIGINT UNSIGNED AUTO_INCREMENT PRIMARY KEY,\n sensor_id INT UNSIGNED NOT NULL,\n drift_date DATE NOT NULL,\n temperature_deviation DECIMAL(5,2) NOT NULL,\n reporting_email VARCHAR(150) NOT NULL,\n log_url VARCHAR(255) NOT NULL,\n readings TEXT NOT NULL,\n created_at DATETIME NOT NULL,\n updated_at DATETIME NOT NULL,\n is_processed TINYINT(1) NOT NULL DEFAULT 0,\n notes TEXT,\n FOREIGN KEY (sensor_id) REFERENCES sensor(sensor_id)\n) ENGINE=InnoDB;\n\nINSERT INTO weather_station (station_name, latitude, longitude, installed_date, created_at, updated_at, is_active, description) VALUES\n('North Ridge', 45.123456, -122.654321, '2023-03-15', '2023-04-01 08:00:00', '2023-04-01 08:00:00', 1, 'High elevation site'),\n('East Valley', 44.987654, -122.112233, '2022-11-20', '2022-12-01 09:15:00', '2022-12-01 09:15:00', 1, NULL);\n\nINSERT INTO sensor (station_id, sensor_type, calibration_date, serial_number, firmware_version, is_active, created_at, updated_at, description) VALUES\n(1, 'Thermometer', '2025-01-01', 'SN-001', 'v1.2.0', 1, '2025-01-02 08:00:00', '2025-01-02 08:00:00', NULL),\n(2, 'Thermometer', '2024-12-10', 'SN-003', 'v1.1.3', 1, '2024-12-12 09:00:00', '2024-12-12 09:00:00', NULL);\n\nINSERT INTO sensor_log (sensor_id, drift_date, temperature_deviation, reporting_email, log_url, readings, created_at, updated_at, is_processed, notes) VALUES\n(1, '2025-07-01', 0.45, 'tech.north@example.com', 'http://logs.example.com/north/2025-07-01', '[21.2,21.5,21.7]', '2025-07-02 08:10:00', '2025-07-02 08:10:00', 1, NULL),\n(1, '2025-07-15', 0.30, 'ops.north@example.com', 'http://logs.example.com/north/2025-07-15', '[21.0,21.1]', '2025-07-16 08:12:00', '2025-07-16 08:12:00', 0, NULL),\n(2, '2025-07-03', -0.20, 'maintenance@eastvalley.org', 'http://logs.example.com/east/2025-07-03', '[19.8,20.0,20.2,20.3]', '2025-07-04 09:00:00', '2025-07-04 09:00:00', 1, NULL);\n\n<|Natural Language Question|>\nPlease retrieve for each weather station the latest sensor-drift date, the average temperature deviation, and the domain extracted from the reporting email within the sensor log URL, omitting any records whose embedded readings array contains fewer than three entries, and return the list ordered by descending average deviation.\n\n<|Start of SQL Query A|>\nWITH filtered_logs AS (\n SELECT\n sl.log_id,\n s.station_id,\n ws.station_name,\n sl.drift_date,\n sl.temperature_deviation,\n SUBSTRING_INDEX(sl.reporting_email, '@', -1) AS email_domain\n FROM sensor_log sl\n JOIN sensor s ON sl.sensor_id = s.sensor_id\n JOIN weather_station ws ON s.station_id = ws.station_id\n WHERE (LENGTH(sl.readings) - LENGTH(REPLACE(sl.readings, ',', ''))) >= 2\n),\nagg AS (\n SELECT\n station_id,\n station_name,\n MAX(drift_date) AS latest_drift_date,\n AVG(temperature_deviation) AS avg_temp_deviation\n FROM filtered_logs\n GROUP BY station_id, station_name\n),\nlatest_domain AS (\n SELECT station_id, email_domain\n FROM (\n SELECT\n fl.station_id,\n fl.email_domain,\n ROW_NUMBER() OVER (PARTITION BY fl.station_id ORDER BY fl.drift_date DESC) AS rn\n FROM filtered_logs fl\n ) t\n WHERE rn = 1\n)\nSELECT\n a.station_id,\n a.station_name,\n a.latest_drift_date,\n ROUND(a.avg_temp_deviation, 2) AS avg_temp_deviation,\n d.email_domain\nFROM agg a\nJOIN latest_domain d ON a.station_id = d.station_id\nORDER BY a.avg_temp_deviation DESC;\n<|End of SQL Query A|>\n\n<|Start of SQL Query B|>\nSELECT\n ws.station_name,\n MAX(sl.drift_date) AS latest_drift_date,\n AVG(sl.temperature_deviation) AS avg_temperature_deviation,\n SUBSTRING_INDEX(\n SUBSTRING_INDEX(sl.reporting_email, '@', -1),\n '.', -2\n ) AS email_domain\nFROM\n weather_station ws\nJOIN\n sensor s ON s.station_id = ws.station_id\nJOIN\n sensor_log sl ON sl.sensor_id = s.sensor_id\nWHERE\n -- Only consider logs whose readings array has 3 or more entries\n (\n LENGTH(sl.readings) - LENGTH(REPLACE(sl.readings, ',', ''))\n ) >= 2\nGROUP BY\n ws.station_id,\n email_domain\nORDER BY\n avg_temperature_deviation DESC;\n<|End of SQL Query B|>", "role": "user", "type": "message"}], "instructions": null, "max_output_tokens": null, "max_tool_calls": null, "metadata": null, "model": null, "parallel_tool_calls": true, "previous_response_id": null, "prompt": null, "reasoning": null, "service_tier": null, "store": null, "temperature": null, "text": null, "tool_choice": "auto", "tools": [], "top_logprobs": null, "top_p": null, "truncation": null, "user": null, "stream": null}, "response": {"id": "resp_05d48bb243f25e94006985206db7fc819ebcdf9f11155a9811", "created_at": 1770332269.0, "error": null, "incomplete_details": null, "instructions": null, "metadata": {}, "model": "gpt-4.1-2025-04-14", "object": "response", "output": [{"id": "msg_05d48bb243f25e94006985206e8270819e9b750d3289adf818", "content": [{"annotations": [], "text": "Let's analyze both queries step by step according to the schema and the requirements.\n\n## Requirements (paraphrased)\n- For **each weather station**:\n - The **latest drift date**\n - The **average temperature deviation** (for that station)\n - The **domain from the reporting email** (extracted from logs)\n - Specifically, \"the domain extracted from the reporting email _within the sensor log URL_\". This is ambiguous, but sample data and both queries extract the domain from the reporting_email, not the log_url.\n - **Exclude any log where readings has fewer than 3 entries** (i.e., number of commas < 2)\n - **Order by descending average deviation**\n\n## Query A Breakdown\n\n- **filtered_logs**:\n - Only logs where readings has 3+ entries (`(LENGTH(readings) - LENGTH(REPLACE(readings,',',''))) >= 2`).\n - Joins all necessary tables.\n - Extracts the domain from reporting_email using `SUBSTRING_INDEX(reporting_email,'@',-1)`.\n\n- **agg**:\n - For each station: computes MAX(drift_date) and AVG(temp_deviation), grouping by station_id and station_name.\n\n- **latest_domain**:\n - For each station, picks the domain from the reporting_email of the most recent log (by drift_date). Uses ROW_NUMBER() to break ties.\n\n- **Final SELECT**:\n - Joins the aggregates (latest drift date/average deviation per station) with the latest email_domain for that station (taken from the most recent drift_date in logs **after filtering**).\n - Orders by descending avg_temp_deviation.\n - Columns: station_id, station_name, latest_drift_date, avg_temp_deviation, email_domain.\n\n---\n\n## Query B Breakdown\n\n- Directly joins all three tables.\n- `WHERE` clause: same readings filter for logs with at least 3 entries.\n- In the SELECT:\n - Aggregates: `MAX(sl.drift_date)` and `AVG(sl.temperature_deviation)`\n - For the **email_domain**: tries to extract the domain (uses `SUBSTRING_INDEX(SUBSTRING_INDEX(...), '.', -2)`)\n- **GROUP BY**: `ws.station_id, email_domain`\n- Orders by descending average temperature deviation.\n- Columns: station_name, latest_drift_date, avg_temperature_deviation, email_domain.\n\n---\n\n## Comparison\n\nLet's check each requirement, field by field:\n\n### 1. Filtering of logs (minimum readings)\n\n**Both queries** use the same:\n```sql\n(LENGTH(readings) - LENGTH(REPLACE(readings,',',''))) >= 2\n```\nThis counts at least two commas (i.e., three entries), so this part is **equivalent**.\n\n---\n\n### 2. Station grouping\n\n- **Query A**: All per-station aggregates are computed _over all logs for that station_, regardless of individual log email domains.\n - One row *per station*.\n- **Query B**: The `GROUP BY` includes both `ws.station_id` **and** `email_domain`.\n - This produces **multiple rows per station** if email_domain varies between logs (e.g. if station 1 has logs from both `foo@bar.com` and `foo@baz.com`).\n\n**Conclusion:** Query B will output multiple rows per station if the reporting_email domains are different in logs for the same station (which may happen per data and schema). Query A always aggregates per station and picks a single latest email_domain.\n\n---\n\n### 3. Latest drift_date\n\n- **Query A:** Uses `MAX(drift_date)` per station (correct), stored in `agg`.\n- **Query B:** Uses `MAX(sl.drift_date)` per `station, email_domain` (so may differ for each domain's logs, not per whole station).\n - **In Query B**, if there are logs for a station with different domains, each gets its own `MAX`.\n\n---\n\n### 4. Average temperature deviation\n\n- **Query A:** Calculates average per station (as expected).\n- **Query B:** Calculates average per station **and domain** (i.e., only logs with a given reporting_email domain).\n - This will split the average if the station has logs with multiple domains.\n\n---\n\n### 5. Email domain extraction\n\n- **Query A:** Uses `SUBSTRING_INDEX(reporting_email,'@',-1)`.\n - Extracts everything after the last '@' (the domain only).\n - Example: `'user@foo.bar.com'` \u2192 `'foo.bar.com'`.\n\n- **Query B:** \n ```sql\n SUBSTRING_INDEX(SUBSTRING_INDEX(sl.reporting_email, '@', -1), '.', -2)\n ```\n - First, takes everything after '@', which is the domain as above, then, from that, takes **the last two segments separated by a period**.\n - So, `'foo.bar.com'` \u2192 `'bar.com'`, `'eastvalley.org'` stays `'eastvalley.org'`.\n\n This is a **semantic difference**\u2014the column will not match for domains with more than two levels (e.g., `sub.example.co.uk` \u2192 `co.uk` in Query B, `sub.example.co.uk` in Query A).\n\n---\n\n### 6. Picking the domain\n\n- **Query A:** For each station, picks the domain from the most recent log (latest drift_date).\n- **Query B:** Does NOT do this. Outputs a row per each domain found in logs for the station.\n\n---\n\n### 7. Output columns' names/order\n\n- **Query A:** station_id, station_name, latest_drift_date, avg_temp_deviation, email_domain\n- **Query B:** station_name, latest_drift_date, avg_temperature_deviation, email_domain\n\nNames and order can be ignored **if** the results are semantically the same (here, they are not).\n\n---\n\n## Final Verdict\n\n### Key Differences:\n\n1. **Grouping:** Query A produces one row per station; Query B produces multiple rows per station (one per email domain encountered in that station's logs). They will output different rows in general.\n2. **Domain extraction:** Query A extracts the whole domain after '@', Query B extracts only the last two domain parts (`foo.bar.com` \u2192 `bar.com`). Semantically different results.\n3. **Email domain selection:** Query A always chooses the domain from the log with the most recent drift_date; Query B simply groups by all domains present (not necessarily latest).\n\n**These differences mean the queries are NOT functionally equivalent.**\n\n---\n\n### **Verdict**\n\nAfter analyzing both queries, my verdict is **[[A!=B]]**.", "type": "output_text", "logprobs": []}], "role": "assistant", "status": "completed", "type": "message"}], "parallel_tool_calls": true, "temperature": 1.0, "tool_choice": "auto", "tools": [], "top_p": 1.0, "background": false, "conversation": null, "max_output_tokens": null, "max_tool_calls": null, "previous_response_id": null, "prompt": null, "prompt_cache_key": null, "reasoning": {"effort": null, "generate_summary": null, "summary": null}, "safety_identifier": null, "service_tier": "default", "status": "completed", "text": {"format": {"type": "text"}, "verbosity": "medium"}, "top_logprobs": 0, "truncation": "disabled", "usage": {"input_tokens": 2373, "input_tokens_details": {"cached_tokens": 0}, "output_tokens": 1392, "output_tokens_details": {"reasoning_tokens": 0}, "total_tokens": 3765}, "user": null, "billing": {"payer": "developer"}, "completed_at": 1770332301, "frequency_penalty": 0.0, "presence_penalty": 0.0, "prompt_cache_retention": null, "store": true}, "verdict_label": "[[A!=B]]"}], "metadata": null} +{"responses_create_params": {"background": null, "include": null, "input": [{"content": "You are a SQL assistant who expertly maps natural language commands and problems into clean, efficient, and well-structured SQL queries. You always carefully examine the database context to include only the relevant tables and fields. You return only SQL code (no explanations outside of SQL code block; only proper comments are permitted). Wrap your SQL query inside a ```sql``` code block.", "role": "system", "type": "message"}, {"content": "postgresql\n\n\nCREATE TABLE users (\n user_id SERIAL PRIMARY KEY,\n username VARCHAR(50) UNIQUE NOT NULL,\n email VARCHAR(150) NOT NULL,\n registration_date DATE NOT NULL,\n is_premium BOOLEAN DEFAULT FALSE\n);\n\nCREATE TABLE posts (\n post_id SERIAL PRIMARY KEY,\n user_id INT REFERENCES users(user_id),\n title VARCHAR(200) NOT NULL,\n content TEXT,\n created_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP,\n view_count INT DEFAULT 0\n);\n\nCREATE TABLE comments (\n comment_id SERIAL PRIMARY KEY,\n post_id INT REFERENCES posts(post_id),\n user_id INT REFERENCES users(user_id),\n comment_text TEXT NOT NULL,\n created_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP\n);\n\nINSERT INTO users (username, email, registration_date, is_premium) VALUES\n('alice_dev', 'alice@example.com', '2024-01-15', TRUE),\n('bob_writer', 'bob@example.com', '2024-02-20', FALSE),\n('carol_blogger', 'carol@example.com', '2024-03-10', TRUE);\n\nINSERT INTO posts (user_id, title, content, view_count) VALUES\n(1, 'Getting Started with PostgreSQL', 'A beginner guide...', 1500),\n(1, 'Advanced SQL Techniques', 'Deep dive into...', 2300),\n(1, 'Database Optimization', 'Tips for performance...', 1800),\n(1, 'SQL Best Practices', 'Industry standards...', 900),\n(1, 'Query Tuning Guide', 'How to optimize...', 1200),\n(2, 'Writing Tips', 'How to write better...', 500),\n(3, 'My First Blog Post', 'Hello world!', 100);\n\nINSERT INTO comments (post_id, user_id, comment_text) VALUES\n(1, 2, 'Great introduction!'),\n(1, 3, 'Very helpful, thanks!'),\n(2, 2, 'Learned a lot from this'),\n(2, 3, 'Can you elaborate on CTEs?'),\n(3, 2, 'Nice tips!'),\n(4, 3, 'Following these now'),\n(5, 2, 'Bookmarked for later');\n\n\n\nFind the average number of comments per post for each user, but only include users who have created at least 5 posts. Show username, total posts, total comments received, and average comments per post.", "role": "user", "type": "message"}], "instructions": null, "max_output_tokens": null, "max_tool_calls": null, "metadata": null, "model": null, "parallel_tool_calls": true, "previous_response_id": null, "prompt": null, "reasoning": null, "service_tier": null, "store": null, "temperature": null, "text": null, "tool_choice": "auto", "tools": [], "top_logprobs": null, "top_p": null, "truncation": null, "user": null, "stream": null}, "response": {"id": "resp_03900f7b4ac1b402006985206b13d481929582cf79184023ee", "created_at": 1770332267.0, "error": null, "incomplete_details": null, "instructions": null, "metadata": {}, "model": "gpt-4.1-2025-04-14", "object": "response", "output": [{"id": "msg_03900f7b4ac1b402006985206b92e08192b6c4e5c4969e5b47", "content": [{"annotations": [], "text": "```sql\nSELECT\n u.username,\n COUNT(p.post_id) AS total_posts,\n COALESCE(SUM(pc.comment_count), 0) AS total_comments_received,\n ROUND(\n COALESCE(SUM(pc.comment_count), 0)::numeric / COUNT(p.post_id), \n 2\n ) AS avg_comments_per_post\nFROM\n users u\nJOIN\n posts p ON u.user_id = p.user_id\nLEFT JOIN (\n -- Count comments for each post\n SELECT post_id, COUNT(*) AS comment_count\n FROM comments\n GROUP BY post_id\n) pc ON p.post_id = pc.post_id\nGROUP BY u.user_id, u.username\nHAVING COUNT(p.post_id) >= 5\nORDER BY u.username;\n```", "type": "output_text", "logprobs": []}], "role": "assistant", "status": "completed", "type": "message"}], "parallel_tool_calls": true, "temperature": 1.0, "tool_choice": "auto", "tools": [], "top_p": 1.0, "background": false, "conversation": null, "max_output_tokens": null, "max_tool_calls": null, "previous_response_id": null, "prompt": null, "prompt_cache_key": null, "reasoning": {"effort": null, "generate_summary": null, "summary": null}, "safety_identifier": null, "service_tier": "default", "status": "completed", "text": {"format": {"type": "text"}, "verbosity": "medium"}, "top_logprobs": 0, "truncation": "disabled", "usage": {"input_tokens": 605, "input_tokens_details": {"cached_tokens": 0}, "output_tokens": 163, "output_tokens_details": {"reasoning_tokens": 0}, "total_tokens": 768}, "user": null, "billing": {"payer": "developer"}, "completed_at": 1770332269, "frequency_penalty": 0.0, "presence_penalty": 0.0, "prompt_cache_retention": null, "store": true}, "reward": 1.0, "uuid": "social-media-001", "sql": "SELECT\n u.username,\n COUNT(DISTINCT p.post_id) AS total_posts,\n COUNT(c.comment_id) AS total_comments,\n ROUND(COUNT(c.comment_id)::NUMERIC / COUNT(DISTINCT p.post_id), 2) AS avg_comments_per_post\nFROM users u\nJOIN posts p ON u.user_id = p.user_id\nLEFT JOIN comments c ON p.post_id = c.post_id\nGROUP BY u.user_id, u.username\nHAVING COUNT(DISTINCT p.post_id) >= 5\nORDER BY avg_comments_per_post DESC;", "model_output": "```sql\nSELECT\n u.username,\n COUNT(p.post_id) AS total_posts,\n COALESCE(SUM(pc.comment_count), 0) AS total_comments_received,\n ROUND(\n COALESCE(SUM(pc.comment_count), 0)::numeric / COUNT(p.post_id), \n 2\n ) AS avg_comments_per_post\nFROM\n users u\nJOIN\n posts p ON u.user_id = p.user_id\nLEFT JOIN (\n -- Count comments for each post\n SELECT post_id, COUNT(*) AS comment_count\n FROM comments\n GROUP BY post_id\n) pc ON p.post_id = pc.post_id\nGROUP BY u.user_id, u.username\nHAVING COUNT(p.post_id) >= 5\nORDER BY u.username;\n```", "extracted_sql": "SELECT\n u.username,\n COUNT(p.post_id) AS total_posts,\n COALESCE(SUM(pc.comment_count), 0) AS total_comments_received,\n ROUND(\n COALESCE(SUM(pc.comment_count), 0)::numeric / COUNT(p.post_id), \n 2\n ) AS avg_comments_per_post\nFROM\n users u\nJOIN\n posts p ON u.user_id = p.user_id\nLEFT JOIN (\n -- Count comments for each post\n SELECT post_id, COUNT(*) AS comment_count\n FROM comments\n GROUP BY post_id\n) pc ON p.post_id = pc.post_id\nGROUP BY u.user_id, u.username\nHAVING COUNT(p.post_id) >= 5\nORDER BY u.username;", "sql_dialect": "postgresql", "sql_context": "CREATE TABLE users (\n user_id SERIAL PRIMARY KEY,\n username VARCHAR(50) UNIQUE NOT NULL,\n email VARCHAR(150) NOT NULL,\n registration_date DATE NOT NULL,\n is_premium BOOLEAN DEFAULT FALSE\n);\n\nCREATE TABLE posts (\n post_id SERIAL PRIMARY KEY,\n user_id INT REFERENCES users(user_id),\n title VARCHAR(200) NOT NULL,\n content TEXT,\n created_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP,\n view_count INT DEFAULT 0\n);\n\nCREATE TABLE comments (\n comment_id SERIAL PRIMARY KEY,\n post_id INT REFERENCES posts(post_id),\n user_id INT REFERENCES users(user_id),\n comment_text TEXT NOT NULL,\n created_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP\n);\n\nINSERT INTO users (username, email, registration_date, is_premium) VALUES\n('alice_dev', 'alice@example.com', '2024-01-15', TRUE),\n('bob_writer', 'bob@example.com', '2024-02-20', FALSE),\n('carol_blogger', 'carol@example.com', '2024-03-10', TRUE);\n\nINSERT INTO posts (user_id, title, content, view_count) VALUES\n(1, 'Getting Started with PostgreSQL', 'A beginner guide...', 1500),\n(1, 'Advanced SQL Techniques', 'Deep dive into...', 2300),\n(1, 'Database Optimization', 'Tips for performance...', 1800),\n(1, 'SQL Best Practices', 'Industry standards...', 900),\n(1, 'Query Tuning Guide', 'How to optimize...', 1200),\n(2, 'Writing Tips', 'How to write better...', 500),\n(3, 'My First Blog Post', 'Hello world!', 100);\n\nINSERT INTO comments (post_id, user_id, comment_text) VALUES\n(1, 2, 'Great introduction!'),\n(1, 3, 'Very helpful, thanks!'),\n(2, 2, 'Learned a lot from this'),\n(2, 3, 'Can you elaborate on CTEs?'),\n(3, 2, 'Nice tips!'),\n(4, 3, 'Following these now'),\n(5, 2, 'Bookmarked for later');", "sql_prompt": "Find the average number of comments per post for each user, but only include users who have created at least 5 posts. Show username, total posts, total comments received, and average comments per post.", "judge_passed": true, "failure_reason": "none", "judge_evaluations": [{"responses_create_params": {"background": null, "include": null, "input": [{"content": "You are an expert SQL judge evaluating the functional equivalence of two SQL queries. You will be given SQL query A and SQL query B. Your task is to determine whether both queries would produce identical result sets when executed against the provided database schema.\n\n## Core Evaluation Principle\n\nTwo SQL queries are functionally equivalent if and only if they return the same rows with the same column values for all possible database states conforming to the schema. Focus on semantic equivalence, not syntactic similarity.\n\n## SQL Equivalence Rules\n\n### Always Equivalent (ignore these differences):\n- **Formatting**: Whitespace, line breaks, capitalization of keywords\n- **Aliasing**: Different table/column aliases (e.g., `e.name` vs `employees.name`)\n- **JOIN syntax**: `FROM a, b WHERE a.id = b.id` \u2261 `FROM a JOIN b ON a.id = b.id`\n- **JOIN keyword**: `JOIN` \u2261 `INNER JOIN`\n- **Explicit vs implicit column lists**: When results are identical\n- **Redundant DISTINCT**: When the query already guarantees uniqueness (e.g., selecting a PRIMARY KEY)\n- **Parentheses**: Extra parentheses that don't change evaluation order\n- **Semicolons**: Trailing semicolon presence/absence\n\n### Equivalent Patterns (require careful analysis):\n- **CTE vs Subquery**: `WITH t AS (SELECT ...) SELECT * FROM t` may equal a nested subquery\n- **EXISTS vs IN vs JOIN**: Often equivalent for the same filtering logic\n- **COALESCE vs CASE**: `COALESCE(a, b)` \u2261 `CASE WHEN a IS NOT NULL THEN a ELSE b END`\n- **Boolean expressions**: `NOT (a AND b)` \u2261 `(NOT a) OR (NOT b)` (De Morgan's laws)\n- **Arithmetic**: `price * 1.1` \u2261 `price + price * 0.1`\n- **Date arithmetic**: Dialect-specific but logically equivalent date calculations\n\n### Never Equivalent (these always matter):\n- **Different columns selected**: `SELECT a, b` \u2260 `SELECT a, c`\n- **Different filtering**: `WHERE x > 5` \u2260 `WHERE x >= 5`\n- **Different aggregations**: `SUM(x)` \u2260 `AVG(x)`\n- **Different grouping**: `GROUP BY a` \u2260 `GROUP BY a, b`\n- **Missing/extra DISTINCT**: When it changes the result set\n- **Different ORDER BY**: When ordering is semantically required by the question\n- **Different LIMIT/OFFSET**: When they affect the result set\n- **NULL handling differences**: When they produce different results\n\n## Dialect-Specific Considerations\n\n### MySQL\n- Backtick quoting for identifiers: `table_name`.`column_name`\n- LIMIT syntax: `LIMIT n` or `LIMIT offset, count`\n- String functions: `SUBSTRING_INDEX()`, `CONCAT()`, `GROUP_CONCAT()`\n- Auto-increment: `AUTO_INCREMENT`\n- Boolean: `TINYINT(1)`, `TRUE`/`FALSE`\n\n### PostgreSQL\n- Double-quote identifiers: \"column\"\n- Type casting: `value::type` or `CAST(value AS type)`\n- Array operations: `ANY()`, `ALL()`, array literals\n- String functions: `SPLIT_PART()`, `STRING_AGG()`\n- Sequences: `SERIAL`, `BIGSERIAL`\n- Boolean: Native `BOOLEAN` type\n\n### SQLite\n- Limited type system (TEXT, INTEGER, REAL, BLOB)\n- String concatenation: `||` operator\n- No native BOOLEAN (uses 0/1)\n- `AUTOINCREMENT` keyword\n- No RIGHT JOIN or FULL OUTER JOIN\n\n## Output Format\n\nAnalyze both queries step by step, then provide your verdict:\n- If the queries are functionally equivalent: [[A=B]]\n- If the queries produce different results: [[A!=B]]\n\nExample: \"After analyzing both queries, my verdict is [[A=B]]\".", "role": "system", "type": "message"}, {"content": "<|SQL Dialect|>\npostgresql\n\n<|Database Schema and Sample Data|>\nCREATE TABLE users (\n user_id SERIAL PRIMARY KEY,\n username VARCHAR(50) UNIQUE NOT NULL,\n email VARCHAR(150) NOT NULL,\n registration_date DATE NOT NULL,\n is_premium BOOLEAN DEFAULT FALSE\n);\n\nCREATE TABLE posts (\n post_id SERIAL PRIMARY KEY,\n user_id INT REFERENCES users(user_id),\n title VARCHAR(200) NOT NULL,\n content TEXT,\n created_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP,\n view_count INT DEFAULT 0\n);\n\nCREATE TABLE comments (\n comment_id SERIAL PRIMARY KEY,\n post_id INT REFERENCES posts(post_id),\n user_id INT REFERENCES users(user_id),\n comment_text TEXT NOT NULL,\n created_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP\n);\n\nINSERT INTO users (username, email, registration_date, is_premium) VALUES\n('alice_dev', 'alice@example.com', '2024-01-15', TRUE),\n('bob_writer', 'bob@example.com', '2024-02-20', FALSE),\n('carol_blogger', 'carol@example.com', '2024-03-10', TRUE);\n\nINSERT INTO posts (user_id, title, content, view_count) VALUES\n(1, 'Getting Started with PostgreSQL', 'A beginner guide...', 1500),\n(1, 'Advanced SQL Techniques', 'Deep dive into...', 2300),\n(1, 'Database Optimization', 'Tips for performance...', 1800),\n(1, 'SQL Best Practices', 'Industry standards...', 900),\n(1, 'Query Tuning Guide', 'How to optimize...', 1200),\n(2, 'Writing Tips', 'How to write better...', 500),\n(3, 'My First Blog Post', 'Hello world!', 100);\n\nINSERT INTO comments (post_id, user_id, comment_text) VALUES\n(1, 2, 'Great introduction!'),\n(1, 3, 'Very helpful, thanks!'),\n(2, 2, 'Learned a lot from this'),\n(2, 3, 'Can you elaborate on CTEs?'),\n(3, 2, 'Nice tips!'),\n(4, 3, 'Following these now'),\n(5, 2, 'Bookmarked for later');\n\n<|Natural Language Question|>\nFind the average number of comments per post for each user, but only include users who have created at least 5 posts. Show username, total posts, total comments received, and average comments per post.\n\n<|Start of SQL Query A|>\nSELECT\n u.username,\n COUNT(DISTINCT p.post_id) AS total_posts,\n COUNT(c.comment_id) AS total_comments,\n ROUND(COUNT(c.comment_id)::NUMERIC / COUNT(DISTINCT p.post_id), 2) AS avg_comments_per_post\nFROM users u\nJOIN posts p ON u.user_id = p.user_id\nLEFT JOIN comments c ON p.post_id = c.post_id\nGROUP BY u.user_id, u.username\nHAVING COUNT(DISTINCT p.post_id) >= 5\nORDER BY avg_comments_per_post DESC;\n<|End of SQL Query A|>\n\n<|Start of SQL Query B|>\nSELECT\n u.username,\n COUNT(p.post_id) AS total_posts,\n COALESCE(SUM(pc.comment_count), 0) AS total_comments_received,\n ROUND(\n COALESCE(SUM(pc.comment_count), 0)::numeric / COUNT(p.post_id), \n 2\n ) AS avg_comments_per_post\nFROM\n users u\nJOIN\n posts p ON u.user_id = p.user_id\nLEFT JOIN (\n -- Count comments for each post\n SELECT post_id, COUNT(*) AS comment_count\n FROM comments\n GROUP BY post_id\n) pc ON p.post_id = pc.post_id\nGROUP BY u.user_id, u.username\nHAVING COUNT(p.post_id) >= 5\nORDER BY u.username;\n<|End of SQL Query B|>", "role": "user", "type": "message"}], "instructions": null, "max_output_tokens": null, "max_tool_calls": null, "metadata": null, "model": null, "parallel_tool_calls": true, "previous_response_id": null, "prompt": null, "reasoning": null, "service_tier": null, "store": null, "temperature": null, "text": null, "tool_choice": "auto", "tools": [], "top_logprobs": null, "top_p": null, "truncation": null, "user": null, "stream": null}, "response": {"id": "resp_09db077bf7af6fe5006985206d7b5c8197804b0272fdd79361", "created_at": 1770332269.0, "error": null, "incomplete_details": null, "instructions": null, "metadata": {}, "model": "gpt-4.1-2025-04-14", "object": "response", "output": [{"id": "msg_09db077bf7af6fe5006985206e650c8197b2eaec21d2894538", "content": [{"annotations": [], "text": "Let's perform an in-depth, step-by-step analysis to determine if Query A and Query B are functionally equivalent according to the specification for the given schema and question.\n\n---\n\n## **Step 1: What Does the Question Ask?**\n\n> Find the average number of comments **per post** for each user, but **only include users who have created at least 5 posts**. Show:\n> - username,\n> - total posts,\n> - total comments received,\n> - and average comments per post.\n\n**\"Total comments received\"** means: for all posts authored by the user, count up the number of comments those posts have received.\n\n---\n\n## **Step 2: Table-by-table Analysis**\n\n### **Query A**\n\n```sql\nSELECT\n u.username,\n COUNT(DISTINCT p.post_id) AS total_posts,\n COUNT(c.comment_id) AS total_comments,\n ROUND(COUNT(c.comment_id)::NUMERIC / COUNT(DISTINCT p.post_id), 2) AS avg_comments_per_post\nFROM users u\nJOIN posts p ON u.user_id = p.user_id\nLEFT JOIN comments c ON p.post_id = c.post_id\nGROUP BY u.user_id, u.username\nHAVING COUNT(DISTINCT p.post_id) >= 5\nORDER BY avg_comments_per_post DESC;\n```\n\n#### **How does this query work?**\n\n- Joins `users` to their `posts` (INNER JOIN: only users with posts)\n- LEFT JOINs comments onto posts (so posts without comments appear, with NULL c)\n- **total_posts**: Counts DISTINCT post IDs for each user (`COUNT(DISTINCT p.post_id)`)\n- **total_comments**: Counts comment rows (even if multiple per post, that's fine)\n- **avg_comments_per_post**: Divides comment count by number of posts (with rounding)\n- **User inclusion**: Only users with at least 5 posts (`HAVING COUNT(DISTINCT p.post_id) >= 5`)\n- **Ordering**: By avg comments per post (high to low)\n\n### **Query B**\n\n```sql\nSELECT\n u.username,\n COUNT(p.post_id) AS total_posts,\n COALESCE(SUM(pc.comment_count), 0) AS total_comments_received,\n ROUND(\n COALESCE(SUM(pc.comment_count), 0)::numeric / COUNT(p.post_id), \n 2\n ) AS avg_comments_per_post\nFROM\n users u\nJOIN\n posts p ON u.user_id = p.user_id\nLEFT JOIN (\n -- Count comments for each post\n SELECT post_id, COUNT(*) AS comment_count\n FROM comments\n GROUP BY post_id\n) pc ON p.post_id = pc.post_id\nGROUP BY u.user_id, u.username\nHAVING COUNT(p.post_id) >= 5\nORDER BY u.username;\n```\n\n#### **How does this query work?**\n\n- Joins `users` to their `posts`\n- LEFT JOINs a derived table that counts comments per post. So, for each post, gets `comment_count` (or NULL if no comments)\n- **total_posts**: `COUNT(p.post_id)` (remember: only inner-joined posts, so all posts)\n- **total_comments_received**: Sums comment counts per post; if no comments, these will be NULL but COALESCE makes them zero (no over-counting)\n- **avg_comments_per_post**: Sums comment counts, divides by number of posts, rounds\n- **User inclusion**: Only users with at least 5 posts (`HAVING COUNT(p.post_id) >= 5`)\n- **Ordering**: By `u.username` alphabetically\n\n---\n\n## **Step 3: Are the Approaches Equivalent?**\n\nLet's check for **possible differences:**\n\n### **A. Count of Posts?**\n\n- **Query A**: `COUNT(DISTINCT p.post_id)`\n- **Query B**: `COUNT(p.post_id)`\n\nThey are functionally **equivalent** for this schema, because `user_id` is a foreign key in `posts`, and `post_id` is PK, so for a given user, p.post_id is unique in rows produced. No risk of double-counting posts in `posts` table.\n\n- In Query A, the `LEFT JOIN` to comments** can cause duplicate post rows** (for posts with >1 comment), so `COUNT(p.post_id)` would double-count posts when a post has multiple comments, hence the use of `COUNT(DISTINCT p.post_id)`. Query B solves this by joining to an aggregate per post (`pc`), so each post appears once regardless of comments.\n\n### **B. Count of Comments?**\n\n- **Query A**: `COUNT(c.comment_id)` on the `LEFT JOIN comments`\n- **Query B**: `SUM(pc.comment_count)`; for each post, counts its comments, then sums for all posts by user\n\nAre these equivalent?\n- In Query A, for each user, the posts are joined with all their comments (so one row per (user, post, comment)), so `COUNT(c.comment_id)` counts total number of joined comments (excluding NULLs: i.e., counts all comments on user's posts). This is correct.\n- In Query B, the outer join ensures that posts with no comments have pc.comment_count as NULL, replaced with 0 by COALESCE, then sum totals.\n\n**These approaches give the same answer.**\n\n### **C. Average Calculation**\n\nBoth queries divide \"total comments received\" by \"total number of posts\", rounding to 2 decimals. Both do numeric cast for division.\n\n### **D. Filtering (HAVING Clause)**\n\n- **Query A:** Uses `COUNT(DISTINCT p.post_id) >= 5`\n- **Query B:** Uses `COUNT(p.post_id) >= 5`\n\nAs above, **Query A must use DISTINCT since its join causes one row per (post, comment)**, whereas Query B, via the derived table, produces only one row per post per user, making `COUNT(p.post_id)` safe.\n\n- With this schema (no duplicates in `posts` for one user, and INNER JOIN to posts), these are functionally equivalent.\n\n### **E. Ordering**\n\n- Query A: \"ORDER BY avg_comments_per_post DESC\"\n- Query B: \"ORDER BY u.username\"\n\n**Unless the ordering is required as part of the result, this is ignorable for set-equivalence.** \nThe question does not specify any required ordering, so this is not a distinguishing factor.\n\n### **F. Column Output**\n\n- Query A: `username`, `total_posts`, `total_comments`, `avg_comments_per_post`\n- Query B: `username`, `total_posts`, `total_comments_received`, `avg_comments_per_post`\n\nJust a column alias difference: both values represent the same number.\n\n---\n\n## **Step 4: Edge Cases & Data-Driven Checks**\n\n- **Users with >=5 posts, some posts with no comments:** Both will include these users and count posts with no comments (with zero comments)\n- **No user has >=5 posts in the current sample data:** Both return an empty set\n- **A user has some posts with no comments:** Both correctly count these as zero comments posts in denominator\n\n---\n\n## **Step 5: Verdict**\n\n- Both queries produce **identical results for all possible database states**, as their aggregate logic matches, their filtering matches, their field selection matches (modulo aliases), and ordering is not required.\n\n---\n\n**After analyzing both queries, my verdict is [[A=B]]**.", "type": "output_text", "logprobs": []}], "role": "assistant", "status": "completed", "type": "message"}], "parallel_tool_calls": true, "temperature": 1.0, "tool_choice": "auto", "tools": [], "top_p": 1.0, "background": false, "conversation": null, "max_output_tokens": null, "max_tool_calls": null, "previous_response_id": null, "prompt": null, "prompt_cache_key": null, "reasoning": {"effort": null, "generate_summary": null, "summary": null}, "safety_identifier": null, "service_tier": "default", "status": "completed", "text": {"format": {"type": "text"}, "verbosity": "medium"}, "top_logprobs": 0, "truncation": "disabled", "usage": {"input_tokens": 1698, "input_tokens_details": {"cached_tokens": 0}, "output_tokens": 1570, "output_tokens_details": {"reasoning_tokens": 0}, "total_tokens": 3268}, "user": null, "billing": {"payer": "developer"}, "completed_at": 1770332300, "frequency_penalty": 0.0, "presence_penalty": 0.0, "prompt_cache_retention": null, "store": true}, "verdict_label": "[[A=B]]"}, {"responses_create_params": {"background": null, "include": null, "input": [{"content": "You are an expert SQL judge evaluating the functional equivalence of two SQL queries. You will be given SQL query A and SQL query B. Your task is to determine whether both queries would produce identical result sets when executed against the provided database schema.\n\n## Core Evaluation Principle\n\nTwo SQL queries are functionally equivalent if and only if they return the same rows with the same column values for all possible database states conforming to the schema. Focus on semantic equivalence, not syntactic similarity.\n\n## SQL Equivalence Rules\n\n### Always Equivalent (ignore these differences):\n- **Formatting**: Whitespace, line breaks, capitalization of keywords\n- **Aliasing**: Different table/column aliases (e.g., `e.name` vs `employees.name`)\n- **JOIN syntax**: `FROM a, b WHERE a.id = b.id` \u2261 `FROM a JOIN b ON a.id = b.id`\n- **JOIN keyword**: `JOIN` \u2261 `INNER JOIN`\n- **Explicit vs implicit column lists**: When results are identical\n- **Redundant DISTINCT**: When the query already guarantees uniqueness (e.g., selecting a PRIMARY KEY)\n- **Parentheses**: Extra parentheses that don't change evaluation order\n- **Semicolons**: Trailing semicolon presence/absence\n\n### Equivalent Patterns (require careful analysis):\n- **CTE vs Subquery**: `WITH t AS (SELECT ...) SELECT * FROM t` may equal a nested subquery\n- **EXISTS vs IN vs JOIN**: Often equivalent for the same filtering logic\n- **COALESCE vs CASE**: `COALESCE(a, b)` \u2261 `CASE WHEN a IS NOT NULL THEN a ELSE b END`\n- **Boolean expressions**: `NOT (a AND b)` \u2261 `(NOT a) OR (NOT b)` (De Morgan's laws)\n- **Arithmetic**: `price * 1.1` \u2261 `price + price * 0.1`\n- **Date arithmetic**: Dialect-specific but logically equivalent date calculations\n\n### Never Equivalent (these always matter):\n- **Different columns selected**: `SELECT a, b` \u2260 `SELECT a, c`\n- **Different filtering**: `WHERE x > 5` \u2260 `WHERE x >= 5`\n- **Different aggregations**: `SUM(x)` \u2260 `AVG(x)`\n- **Different grouping**: `GROUP BY a` \u2260 `GROUP BY a, b`\n- **Missing/extra DISTINCT**: When it changes the result set\n- **Different ORDER BY**: When ordering is semantically required by the question\n- **Different LIMIT/OFFSET**: When they affect the result set\n- **NULL handling differences**: When they produce different results\n\n## Dialect-Specific Considerations\n\n### MySQL\n- Backtick quoting for identifiers: `table_name`.`column_name`\n- LIMIT syntax: `LIMIT n` or `LIMIT offset, count`\n- String functions: `SUBSTRING_INDEX()`, `CONCAT()`, `GROUP_CONCAT()`\n- Auto-increment: `AUTO_INCREMENT`\n- Boolean: `TINYINT(1)`, `TRUE`/`FALSE`\n\n### PostgreSQL\n- Double-quote identifiers: \"column\"\n- Type casting: `value::type` or `CAST(value AS type)`\n- Array operations: `ANY()`, `ALL()`, array literals\n- String functions: `SPLIT_PART()`, `STRING_AGG()`\n- Sequences: `SERIAL`, `BIGSERIAL`\n- Boolean: Native `BOOLEAN` type\n\n### SQLite\n- Limited type system (TEXT, INTEGER, REAL, BLOB)\n- String concatenation: `||` operator\n- No native BOOLEAN (uses 0/1)\n- `AUTOINCREMENT` keyword\n- No RIGHT JOIN or FULL OUTER JOIN\n\n## Output Format\n\nAnalyze both queries step by step, then provide your verdict:\n- If the queries are functionally equivalent: [[A=B]]\n- If the queries produce different results: [[A!=B]]\n\nExample: \"After analyzing both queries, my verdict is [[A=B]]\".", "role": "system", "type": "message"}, {"content": "<|SQL Dialect|>\npostgresql\n\n<|Database Schema and Sample Data|>\nCREATE TABLE users (\n user_id SERIAL PRIMARY KEY,\n username VARCHAR(50) UNIQUE NOT NULL,\n email VARCHAR(150) NOT NULL,\n registration_date DATE NOT NULL,\n is_premium BOOLEAN DEFAULT FALSE\n);\n\nCREATE TABLE posts (\n post_id SERIAL PRIMARY KEY,\n user_id INT REFERENCES users(user_id),\n title VARCHAR(200) NOT NULL,\n content TEXT,\n created_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP,\n view_count INT DEFAULT 0\n);\n\nCREATE TABLE comments (\n comment_id SERIAL PRIMARY KEY,\n post_id INT REFERENCES posts(post_id),\n user_id INT REFERENCES users(user_id),\n comment_text TEXT NOT NULL,\n created_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP\n);\n\nINSERT INTO users (username, email, registration_date, is_premium) VALUES\n('alice_dev', 'alice@example.com', '2024-01-15', TRUE),\n('bob_writer', 'bob@example.com', '2024-02-20', FALSE),\n('carol_blogger', 'carol@example.com', '2024-03-10', TRUE);\n\nINSERT INTO posts (user_id, title, content, view_count) VALUES\n(1, 'Getting Started with PostgreSQL', 'A beginner guide...', 1500),\n(1, 'Advanced SQL Techniques', 'Deep dive into...', 2300),\n(1, 'Database Optimization', 'Tips for performance...', 1800),\n(1, 'SQL Best Practices', 'Industry standards...', 900),\n(1, 'Query Tuning Guide', 'How to optimize...', 1200),\n(2, 'Writing Tips', 'How to write better...', 500),\n(3, 'My First Blog Post', 'Hello world!', 100);\n\nINSERT INTO comments (post_id, user_id, comment_text) VALUES\n(1, 2, 'Great introduction!'),\n(1, 3, 'Very helpful, thanks!'),\n(2, 2, 'Learned a lot from this'),\n(2, 3, 'Can you elaborate on CTEs?'),\n(3, 2, 'Nice tips!'),\n(4, 3, 'Following these now'),\n(5, 2, 'Bookmarked for later');\n\n<|Natural Language Question|>\nFind the average number of comments per post for each user, but only include users who have created at least 5 posts. Show username, total posts, total comments received, and average comments per post.\n\n<|Start of SQL Query A|>\nSELECT\n u.username,\n COUNT(p.post_id) AS total_posts,\n COALESCE(SUM(pc.comment_count), 0) AS total_comments_received,\n ROUND(\n COALESCE(SUM(pc.comment_count), 0)::numeric / COUNT(p.post_id), \n 2\n ) AS avg_comments_per_post\nFROM\n users u\nJOIN\n posts p ON u.user_id = p.user_id\nLEFT JOIN (\n -- Count comments for each post\n SELECT post_id, COUNT(*) AS comment_count\n FROM comments\n GROUP BY post_id\n) pc ON p.post_id = pc.post_id\nGROUP BY u.user_id, u.username\nHAVING COUNT(p.post_id) >= 5\nORDER BY u.username;\n<|End of SQL Query A|>\n\n<|Start of SQL Query B|>\nSELECT\n u.username,\n COUNT(DISTINCT p.post_id) AS total_posts,\n COUNT(c.comment_id) AS total_comments,\n ROUND(COUNT(c.comment_id)::NUMERIC / COUNT(DISTINCT p.post_id), 2) AS avg_comments_per_post\nFROM users u\nJOIN posts p ON u.user_id = p.user_id\nLEFT JOIN comments c ON p.post_id = c.post_id\nGROUP BY u.user_id, u.username\nHAVING COUNT(DISTINCT p.post_id) >= 5\nORDER BY avg_comments_per_post DESC;\n<|End of SQL Query B|>", "role": "user", "type": "message"}], "instructions": null, "max_output_tokens": null, "max_tool_calls": null, "metadata": null, "model": null, "parallel_tool_calls": true, "previous_response_id": null, "prompt": null, "reasoning": null, "service_tier": null, "store": null, "temperature": null, "text": null, "tool_choice": "auto", "tools": [], "top_logprobs": null, "top_p": null, "truncation": null, "user": null, "stream": null}, "response": {"id": "resp_0b92dd72a3252dc6006985208c5538819cb852ecccb6930228", "created_at": 1770332300.0, "error": null, "incomplete_details": null, "instructions": null, "metadata": {}, "model": "gpt-4.1-2025-04-14", "object": "response", "output": [{"id": "msg_0b92dd72a3252dc6006985208cb9dc819cbb1ca5c1bbce6ea2", "content": [{"annotations": [], "text": "Let's analyze SQL Query A and SQL Query B step by step based on the schema, question, and requirements.\n\n---\n\n## Step 1: Interpretation of the Question\n\n> Find the average number of comments per post for each user, **but only include users who have created at least 5 posts**. Show `username`, total posts, **total comments received**, and **average comments per post**.\n\n**Clarifications**:\n- \"Total comments received\": the sum of all comments on a user's posts, i.e., all comments where the commented post was authored by the user.\n- \"Average comments per post\": (total comments received) \u00f7 (number of posts).\n- Return `username`, total posts, total comments, average per post.\n- Only users with *at least 5 posts*.\n- Result should be per user, not per post.\n\n---\n\n## Step 2: Analyze Query A\n\n```sql\nSELECT\n u.username,\n COUNT(p.post_id) AS total_posts,\n COALESCE(SUM(pc.comment_count), 0) AS total_comments_received,\n ROUND(\n COALESCE(SUM(pc.comment_count), 0)::numeric / COUNT(p.post_id), \n 2\n ) AS avg_comments_per_post\nFROM\n users u\nJOIN\n posts p ON u.user_id = p.user_id\nLEFT JOIN (\n -- Count comments for each post\n SELECT post_id, COUNT(*) AS comment_count\n FROM comments\n GROUP BY post_id\n) pc ON p.post_id = pc.post_id\nGROUP BY u.user_id, u.username\nHAVING COUNT(p.post_id) >= 5\nORDER BY u.username;\n```\n\n### How Query A Works\n- Joins users with their posts.\n- For each post, LEFT JOINs summarized comment counts (from a subquery giving `post_id, comment_count`).\n- **For each user**:\n - Counts total posts (`COUNT(p.post_id)`).\n - Sums comment counts from `pc`; missing posts (with zero comments) are handled by `COALESCE`.\n - Average comments per post: total comments received / total posts, rounded.\n- **Filters users with \u22655 posts**.\n- Output columns match requirements (username, total posts, total comments, avg).\n- **ORDER BY username**.\n\n---\n\n## Step 3: Analyze Query B\n\n```sql\nSELECT\n u.username,\n COUNT(DISTINCT p.post_id) AS total_posts,\n COUNT(c.comment_id) AS total_comments,\n ROUND(COUNT(c.comment_id)::NUMERIC / COUNT(DISTINCT p.post_id), 2) AS avg_comments_per_post\nFROM users u\nJOIN posts p ON u.user_id = p.user_id\nLEFT JOIN comments c ON p.post_id = c.post_id\nGROUP BY u.user_id, u.username\nHAVING COUNT(DISTINCT p.post_id) >= 5\nORDER BY avg_comments_per_post DESC;\n```\n\n### How Query B Works\n- Joins users to posts (each user's post appears).\n- LEFT JOINs comments directly, matching each post to all of its comments.\n- For each user:\n - `COUNT(DISTINCT p.post_id)`: number of posts per user.\n - `COUNT(c.comment_id)`: counts all comments attached to these posts (i.e., total comments received).\n - `ROUND(...)`: average comments per post.\n- **Filters users with \u22655 posts**.\n- **ORDER BY avg_comments_per_post DESC**.\n\n---\n\n## Step 4: Spot Differences\n\n**a) Calculating \"total posts\"**\n- Query A: `COUNT(p.post_id)` -- no DISTINCT, but `p.post_id` is unique per post, so this is legitimate.\n- Query B: `COUNT(DISTINCT p.post_id)` -- in case of multiple comments per post, prevents double-counting.\n - **Is this necessary?** Let's check.\n - When a post has multiple comments, due to the JOIN, that post's row(s) will repeat \u2014 so, without DISTINCT, `COUNT(p.post_id)` would overcount. Therefore, Query A is safe because the SUM is only over the results of `pc` (already summarized), so Query A avoids the duplicate row problem. Query B defends against double-counting using DISTINCT, appropriately.\n\n**b) Calculating \"total comments received\"**\n- Query A: `COALESCE(SUM(pc.comment_count), 0)`\n - Each `p` is joined to its `pc.comment_count` (count of comments per post). Zero comments are handled with COALESCE.\n- Query B: `COUNT(c.comment_id)`\n - For each (user, post, comment) row, non-NULL `c.comment_id` increments the count.\n- **Are these the same?**\n - Yes: for a user with 3 posts, where one has 2 comments, one has 1, and one has 0, Query A will sum (2 + 1 + 0) = 3, Query B's join will deliver 2 rows for post1, 1 for post2, none for post3 (with LEFT JOIN), so COUNT will be 3 \u2014 **they are equivalent**.\n\n**c) Calculating \"average comments per post\"**\n- Identically computed: (total comments) / (total posts), rounded to 2 decimals.\n\n**d) Filtering**\n- Both use HAVING clause to ensure at least 5 posts (A: `COUNT(p.post_id)`, B: `COUNT(DISTINCT p.post_id)`). In Query A, double-counting can't occur (by construction via GROUP BY + left join to pre-aggregation), so `COUNT(p.post_id)` is functionally identical to `COUNT(DISTINCT p.post_id)`.\n\n**e) ORDER BY**\n- Query A: `ORDER BY u.username`\n- Query B: `ORDER BY avg_comments_per_post DESC`\n\n> Per question, the required **ordering is not specified**. If the question had said \"order by average comments per post descending\" (or by username), this would matter. But for **result set equivalence**, SQL result ordering is not considered unless ORDER BY is semantically required. **ORDER BY differences can be ignored** unless required for equivalence.\n\n**f) Column naming**\n- In Query A, `total_comments_received`, in Query B, `total_comments`. Since the semantic meaning is the same, this difference is ignorable.\n\n---\n\n## Step 5: Edge Cases\n\n- **Posts with zero comments**: Both handle correctly. Query A's `COALESCE(SUM(pc.comment_count), 0)` covers posts with no comments. Query B's LEFT JOIN ensures that posts with zero comments aren't missed, and `COUNT(c.comment_id)` skips NULLs.\n- **Users with exactly 5 posts**: Both handle `>= 5`.\n- **Users with no posts**: Not possible after HAVING.\n\n---\n\n## Step 6: Test on Sample Data\n\nOnly user 1 (\"alice_dev\") has 5+ posts. Checking both queries on sample data for Alice:\n\n- Posts: 5\n- Comments received: For Alice's posts:\n - post 1: 2 comments\n - post 2: 2 comments\n - post 3: 1 comment\n - post 4: 1 comment\n - post 5: 1 comment\n - **Total: 2+2+1+1+1=7**\n- Average: 7/5 = 1.40\n\nBoth queries will return a single row for Alice:\n| username | total_posts | total_comments | avg_comments_per_post |\n|------------|-------------|----------------|----------------------|\n| alice_dev | 5 | 7 | 1.40 |\n\n---\n\n## Verdict\n\n**After analyzing both queries, they implement the same grouping, filtering, and calculation logic, and produce the same results for all possible data sets conforming to the schema and intent. Differences in ordering and column naming do not affect result set equivalence per usual SQL semantics (unless ordering is required by the question, which it is not here).**\n\n**Therefore:**\n\n> After analyzing both queries, my verdict is [[A=B]]", "type": "output_text", "logprobs": []}], "role": "assistant", "status": "completed", "type": "message"}], "parallel_tool_calls": true, "temperature": 1.0, "tool_choice": "auto", "tools": [], "top_p": 1.0, "background": false, "conversation": null, "max_output_tokens": null, "max_tool_calls": null, "previous_response_id": null, "prompt": null, "prompt_cache_key": null, "reasoning": {"effort": null, "generate_summary": null, "summary": null}, "safety_identifier": null, "service_tier": "default", "status": "completed", "text": {"format": {"type": "text"}, "verbosity": "medium"}, "top_logprobs": 0, "truncation": "disabled", "usage": {"input_tokens": 1698, "input_tokens_details": {"cached_tokens": 1024}, "output_tokens": 1713, "output_tokens_details": {"reasoning_tokens": 0}, "total_tokens": 3411}, "user": null, "billing": {"payer": "developer"}, "completed_at": 1770332336, "frequency_penalty": 0.0, "presence_penalty": 0.0, "prompt_cache_retention": null, "store": true}, "verdict_label": "[[A=B]]"}], "metadata": null}